{ "model": { "rank": 4460, "code": "thing-q133871", "model_id": "vr.tr.normal-distribution", "name": "normal distribution", "purpose": "Enable an agent to identify a normal probability law, interpret its parameters, assess whether its use is justified, and perform probability calculations with explicit assumptions.", "family": "Thing Registry", "category": "Cross-cutting context", "status": "research-draft", "kind": "thing", "plane": "XCT", "domain": "XCT.QLT", "industry": "", "version": "", "url": "/models/thing/q133871/", "tier": 2, "score": 75, "payload": { "layer": "wikidata", "aliases": [ "standardized normal distribution" ], "aliasCount": 1, "merged": 1, "knownIn": 75, "facets": null, "markers": [], "lexicalClass": "", "senseRank": null, "alsoRegisteredAs": null, "source": { "dataset": "wikidata", "item": "Q133871", "url": "https://www.wikidata.org/wiki/Q133871", "license": "CC0 1.0" } }, "research": { "vercy": "1.0-draft", "publication": { "status": "research-draft", "adjudicationStatus": "unreviewed", "publishableCanonical": false, "generatedAt": "2026-09-09T20:16:31Z", "providers": [ "Codex" ], "breadth": "recalled by Codex without web access - no source was read", "missingProviders": [], "pass": 2, "cost": { "grok": { "seconds": 21.1, "error": "Reading additional input from stdin...\nOpenAI Codex v0.153.4\n--------\nworkdir: R:\\02_PROJECTS\\02_Meta_Models_Platforms\\Ver.cy\\current\\thing-registry-backlog\nmodel: gpt-6-astra\nprovider: openai\napproval: never\nsandbox: read-only\nreasoning effort: none\nreasoning summaries: none\nsession id: 01a087cf-ac94-7063-b082-8dc8c903bdca\n--------\nuser\nDescribe what is already known about one registered thing. Answer as JSON only, no prose around it.\n\nThing: normal distribution\nSense to describe: (none recorded)\nDomain code: XCT.REL\nAlso known as: (none)\n\n\nContext for this batch of 695 things:\n# Batch 004: 1", "usd": 0, "recall": true }, "codex": { "seconds": 62.8, "error": "Reading additional input from stdin...\nOpenAI Codex v0.153.4\n--------\nworkdir: R:\\02_PROJECTS\\02_Meta_Models_Platforms\\Ver.cy\\current\\thing-registry-backlog\nmodel: gpt-6-astra\nprovider: openai\napproval: never\nsandbox: read-only\nreasoning effort: none\nreasoning summaries: none\nsession id: 01a087cf-ac92-7d21-9e53-25157bdc7511\n--------\nuser\nYou are drafting a Vercy meta-model for one registered thing. Answer as JSON only, no prose around it.\n\nThing: normal distribution\nRegistry id: vr.tr.normal-distribution\nPlane / domain: XCT / XCT.REL\nRegistry definition: (none recorded)\nNames folded into this " } } }, "metaModel": { "id": "THING-Q133871", "registryId": "vr.tr.normal-distribution", "name": "normal distribution", "version": "0.1.0-research.1", "entryKind": "thing", "family": "Thing Registry", "domain": [ "XCT.REL" ], "status": "research-draft" }, "canonicalUrl": "https://ver.cy/models/thing/q133871/", "model": { "registry_id": "vr.tr.normal-distribution", "name": "normal distribution", "purpose": "Enable an agent to identify a normal probability law, interpret its parameters, assess whether its use is justified, and perform probability calculations with explicit assumptions.", "definition": "The normal distribution is a continuous probability distribution on the real line with density f(x) = exp(-(x-μ)²/(2σ²))/(σ√(2π)), where μ is its mean and σ² > 0 is its variance.", "scope_statement": "This model owns the univariate normal distribution as an abstract family of continuous probability laws and the conditions attached to using a member of that family as an exact model or approximation.", "in_scope": [ "The univariate Gaussian family, its density, support, and parameter restrictions", "Location, scale, standardisation, and parameter conventions", "Probabilities, quantiles, moments, and transformations within the normal family", "Exact normality assumptions, empirical adequacy, and normal approximations", "Conditions governing normal sums and conditional interpretation of calculated results" ], "out_of_scope": [ "Multivariate Gaussian distributions and Gaussian processes", "Probability theory and continuous distributions in general", "Complete models of datasets, measurement systems, or data collection", "Complete statistical procedures such as regression, hypothesis testing, and experimental design", "Lognormal, truncated normal, folded normal, and Gaussian mixture distributions as separate families" ], "distinguishing_features": [ "A nondegenerate member has density f(x) = exp(-(x-mu)^2/(2*sigma^2))/(sigma*sqrt(2*pi)) for every real x, with sigma > 0.", "Its log-density is exactly quadratic in x; symmetry and a bell-shaped appearance alone do not establish normality.", "The mean, median, and unique mode coincide at mu, and variance is sigma^2; these properties are necessary but not sufficient for normality.", "Standardising X by Z = (X-mu)/sigma yields the standard normal law, independent of the original location and scale.", "It assigns positive probability to every nonempty open interval on the real line, distinguishing it from bounded, truncated, discrete, and strictly positive distributions." ], "characteristics": [ { "name": "Location parameter", "kind": "measurement", "unit_or_values": "mu: any finite real value, in the units of the represented variable", "why_it_matters": "Fixes the centre, expectation, median, and mode of the distribution." }, { "name": "Standard deviation", "kind": "measurement", "unit_or_values": "sigma > 0, in the units of the represented variable", "why_it_matters": "Controls dispersion and converts deviations into dimensionless standard scores." }, { "name": "Variance", "kind": "measurement", "unit_or_values": "sigma^2 > 0, in squared units of the represented variable", "why_it_matters": "Enters the density and variance propagation formulas; confusing it with standard deviation changes every calculation." }, { "name": "Parameter notation", "kind": "category", "unit_or_values": "Explicit declaration of whether the second parameter denotes variance, standard deviation, or precision", "why_it_matters": "Prevents incompatible interpretations of normal-distribution notation and software arguments." }, { "name": "Represented quantity", "kind": "relation", "unit_or_values": "Random variable, measurement error, residual, estimator, or other explicitly identified quantity", "why_it_matters": "Normality of one quantity does not establish normality of related observations or derived quantities." }, { "name": "Conditioning context", "kind": "relation", "unit_or_values": "Unconditional law or law conditional on specified variables, groups, and conditions", "why_it_matters": "Conditional normal distributions can combine into a marginal distribution that is not normal." }, { "name": "Modelling status", "kind": "state", "unit_or_values": "Exact by mathematical construction, assumed, empirically assessed, approximate, or rejected for a stated use", "why_it_matters": "Separates properties of the mathematical law from evidence supporting an application." }, { "name": "Parameter knowledge", "kind": "state", "unit_or_values": "Specified, estimated with documented method and uncertainty, or unknown", "why_it_matters": "Plug-in probability calculations can conceal uncertainty arising from estimated parameters." }, { "name": "Use-specific adequacy", "kind": "state", "unit_or_values": "Unassessed, adequate within stated tolerances, or inadequate for a specified probability region and task", "why_it_matters": "A model that describes the centre acceptably may still misrepresent decision-relevant tails." } ], "affordances": [ "Instantiate a normal law from valid parameters and an explicit parameter convention.", "Standardise values and convert standard scores back into the original units.", "Calculate interval probabilities, tail probabilities, and quantiles.", "Propagate a normal law through affine transformations and justified combinations of normal variables.", "Assess a normal assumption or approximation against support constraints, diagnostics, and the intended calculation.", "Generate simulated draws while recording the parameters and reproducibility settings." ] }, "sources": [], "structure": { "bundles": [ { "id": "normal-family-identity", "name": "Normal family identity", "description": "Establishes which mathematical law is represented and distinguishes it from visually similar distributions.", "rationale": "An agent must recognise the family through its defining structure rather than through an informal bell-curve description.", "layers": [ { "id": "defining-law", "name": "Defining law", "description": "Records the density, support, and admissible parameters of a univariate normal law.", "findings": [ { "id": "density-and-support", "name": "Density and full real support", "description": "The ordinary nondegenerate normal family has the stated Gaussian density on the entire real line, with finite mu and strictly positive sigma.", "questions": [ { "text": "Does the proposed law have the Gaussian density for every real x, with sigma strictly positive?", "kind": "definition", "id": "density-and-support-q01" }, { "text": "Do bounds or discreteness of the represented quantity restrict normality to an approximation?", "kind": "boundary", "id": "density-and-support-q02" } ] } ] }, { "id": "nearby-family-boundaries", "name": "Nearby family boundaries", "description": "Separates a normal law from related distributions and exceptional conventions.", "findings": [ { "id": "normal-versus-derived-laws", "name": "Normal versus derived laws", "description": "Truncation, taking an absolute value, exponentiation, and mixing can produce laws outside the univariate normal family; a zero-variance point mass requires an explicit convention.", "questions": [ { "text": "Has truncation, censoring, folding, exponentiation, or mixing changed the law or the observation process?", "kind": "boundary", "id": "normal-versus-derived-laws-q01" }, { "text": "Does the source use the ordinary sigma > 0 definition or an extended convention admitting a degenerate Gaussian point mass?", "kind": "provenance", "id": "normal-versus-derived-laws-q02" } ] } ] } ] }, { "id": "parameters-and-standardisation", "name": "Parameters and standardisation", "description": "Makes location, dispersion, units, and parameter knowledge unambiguous.", "rationale": "Normal calculations depend on correctly interpreting two parameters and distinguishing their specified values from estimates.", "layers": [ { "id": "location-scale-conventions", "name": "Location and scale conventions", "description": "Connects normal parameters to their mathematical meanings and physical units.", "findings": [ { "id": "parameter-meaning", "name": "Parameter meaning", "description": "Mu is the expectation and centre; sigma is standard deviation; sigma squared is variance; precision, when used, is reciprocal variance.", "questions": [ { "text": "Does each parameter field or notation denote location, standard deviation, variance, or precision?", "kind": "definition", "id": "parameter-meaning-q01" }, { "text": "Are location and scale expressed in compatible units, with variance expressed in squared units?", "kind": "measurement", "id": "parameter-meaning-q02" } ] } ] }, { "id": "standard-scores-and-estimates", "name": "Standard scores and estimates", "description": "Records standardisation and the evidential status of the values used.", "findings": [ { "id": "standardisation-with-parameter-status", "name": "Standardisation with parameter status", "description": "For X distributed as N(mu, sigma squared), (X-mu)/sigma is standard normal. Substituting estimated parameters does not automatically preserve that exact sampling law.", "questions": [ { "text": "Are mu and sigma specified population parameters or estimates, and where did their values come from?", "kind": "provenance", "id": "standardisation-with-parameter-status-q01" }, { "text": "Does the intended use require accounting for parameter estimation when interpreting a standardised value?", "kind": "action", "id": "standardisation-with-parameter-status-q02" } ] } ] } ] }, { "id": "probabilities-and-numerical-use", "name": "Probabilities and numerical use", "description": "Connects the normal law to events, quantiles, and reliable numerical evaluation.", "rationale": "An agent needs to translate a practical question into the correct probability operation and retain accuracy in extreme tails.", "layers": [ { "id": "events-and-quantiles", "name": "Events and quantiles", "description": "Defines the event or probability threshold being evaluated.", "findings": [ { "id": "probability-versus-density", "name": "Probability versus density", "description": "Interval probabilities come from cumulative probabilities or integration, not density heights; every individual point has probability zero.", "questions": [ { "text": "Is the request for a density, interval probability, one-sided tail, two-sided event, or quantile?", "kind": "definition", "id": "probability-versus-density-q01" }, { "text": "What thresholds and units define the event, or what cumulative probability defines the requested quantile?", "kind": "measurement", "id": "probability-versus-density-q02" } ] } ] }, { "id": "tail-computation", "name": "Tail computation", "description": "Captures numerical precision requirements where direct probability subtraction may fail.", "findings": [ { "id": "stable-extreme-tail-evaluation", "name": "Stable extreme-tail evaluation", "description": "Extreme upper-tail probabilities can lose numerical accuracy when computed as one minus a rounded cumulative probability; survival and log-probability functions support more stable evaluation.", "questions": [ { "text": "How small a probability or how extreme a quantile must the calculation resolve?", "kind": "measurement", "id": "stable-extreme-tail-evaluation-q01" }, { "text": "Should the implementation use a survival function, log-probability, or an inverse-tail routine to meet that precision requirement?", "kind": "action", "id": "stable-extreme-tail-evaluation-q02" } ] } ] } ] }, { "id": "transformations-and-combinations", "name": "Transformations and combinations", "description": "States when operations preserve normality and how parameters propagate.", "rationale": "Normal inputs alone do not justify treating every transformed or aggregated output as normal.", "layers": [ { "id": "affine-transformations", "name": "Affine transformations", "description": "Handles changes of origin, scale, and units.", "findings": [ { "id": "affine-closure", "name": "Affine closure", "description": "For nonzero a, if X is normal then aX+b is normal with mean a*mu+b and variance a squared times sigma squared; a=0 produces a point mass.", "questions": [ { "text": "Is the proposed transformation affine, and what are its coefficients?", "kind": "definition", "id": "affine-closure-q01" }, { "text": "What transformed mean and variance follow, and does the result remain nondegenerate?", "kind": "measurement", "id": "affine-closure-q02" } ] } ] }, { "id": "sums-and-dependence", "name": "Sums and dependence", "description": "Records the assumptions required to combine normal variables.", "findings": [ { "id": "justified-normal-combinations", "name": "Justified normal combinations", "description": "Independent normal variables have normal sums. Joint normality also supports normal linear combinations, but marginal normality alone is insufficient; covariance terms affect the resulting variance.", "questions": [ { "text": "What establishes independence or joint normality of the variables being combined?", "kind": "provenance", "id": "justified-normal-combinations-q01" }, { "text": "Which weights and covariance terms determine the combination's mean and variance?", "kind": "measurement", "id": "justified-normal-combinations-q02" } ] } ] } ] }, { "id": "normality-and-approximation-warrant", "name": "Normality and approximation warrant", "description": "Assesses why a normal law applies and where the justification stops.", "rationale": "The mathematical family's exact properties must be separated from the adequacy of using it for observations, errors, or sampling distributions.", "layers": [ { "id": "empirical-normality", "name": "Empirical normality", "description": "Relates observed evidence to a specific normality claim and intended use.", "findings": [ { "id": "target-specific-diagnostics", "name": "Target-specific diagnostics", "description": "Quantile plots, distributional tests, and substantive constraints can assess departures from a normal model. Finite data cannot establish exact normality, and failure to reject a test is not proof.", "questions": [ { "text": "Does the normality claim concern raw observations, conditional errors, residuals, or a sampling distribution?", "kind": "boundary", "id": "target-specific-diagnostics-q01" }, { "text": "What diagnostics and support constraints bear on the claim, especially in the probability region used for decisions?", "kind": "measurement", "id": "target-specific-diagnostics-q02" }, { "text": "Which observed departures would make the normal model inadequate for the intended calculation?", "kind": "action", "id": "target-specific-diagnostics-q03" } ] } ] }, { "id": "limit-based-approximations", "name": "Limit-based approximations", "description": "Makes central-limit and related approximation claims conditional on their actual assumptions.", "findings": [ { "id": "central-limit-conditions-and-error", "name": "Central-limit conditions and error", "description": "Central limit theorems can justify normal limits for appropriately standardised sums under specified conditions; they neither make the original observations normal nor guarantee adequate finite-sample tail accuracy.", "questions": [ { "text": "Which limit theorem supports this approximation, and are its moment and dependence assumptions satisfied?", "kind": "provenance", "id": "central-limit-conditions-and-error-q01" }, { "text": "What evidence or error bound supports accuracy at the actual sample size and requested probability region?", "kind": "measurement", "id": "central-limit-conditions-and-error-q02" }, { "text": "For a discrete target, is a continuity correction or direct calculation needed to meet the required accuracy?", "kind": "action", "id": "central-limit-conditions-and-error-q03" } ] } ] } ] }, { "id": "evidence-and-external-alignment", "name": "Evidence and external alignment", "description": "What the world already says about this thing, gathered so the model can be checked against it.", "rationale": "A model that cannot be lined up against existing standards, identifiers and practice cannot be adopted by anyone who already uses them.", "layers": [ { "id": "reported-evidence", "name": "Reported evidence", "description": "Findings from the breadth pass, kept separate from the structural claims.", "findings": [ { "id": "evidence-confidence-notes", "name": "Check these first", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "The sense covered is the probability distribution in mathematics and statistics.", "The definition uses the nondegenerate univariate convention; some treatments also admit zero-variance Gaussian distributions as point masses.", "These are recalled mathematical facts, not findings from sources consulted for this response; suitability for any particular dataset requires checking." ], "questions": [ { "id": "evidence-confidence-notes-q01", "text": "Which of these check these first hold for the sense of normal distribution this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-kinds", "name": "Kinds and varieties", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Standard normal distribution, with μ = 0 and σ² = 1", "General univariate normal distribution", "Multivariate normal distribution, extending the concept to jointly Gaussian random vectors" ], "questions": [ { "id": "evidence-kinds-q01", "text": "Which of these kinds and varieties hold for the sense of normal distribution this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-identifiers", "name": "Identifiers and schemes", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ { "scheme": "Conventional mathematical notation", "value_or_pattern": "N(μ, σ²)", "note": "Commonly specifies mean and variance; some sources instead use the standard deviation as the second parameter, so conventions must be checked." } ], "questions": [ { "id": "evidence-identifiers-q01", "text": "Which of these identifiers and schemes hold for the sense of normal distribution this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-real-world-use", "name": "Real-world use", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Modelling approximately symmetric measurement errors and variation around a mean", "Approximating sampling distributions through central limit theorems when their conditions hold", "Constructing statistical confidence intervals and hypothesis tests", "Modelling conditional errors in Gaussian linear regression", "Calculating probabilities and control limits in statistical process control when a normal model is appropriate" ], "questions": [ { "id": "evidence-real-world-use-q01", "text": "Which of these real-world use hold for the sense of normal distribution this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-measurements", "name": "Typical measurements", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ { "quantity": "Mean μ", "typical_range": "Any real number", "unit": "Same unit as the random variable" }, { "quantity": "Standard deviation σ", "typical_range": "Any positive real number", "unit": "Same unit as the random variable" }, { "quantity": "Variance σ²", "typical_range": "Any positive real number", "unit": "Square of the random variable's unit" }, { "quantity": "Skewness", "typical_range": "Exactly 0", "unit": "Dimensionless" }, { "quantity": "Excess kurtosis", "typical_range": "Exactly 0", "unit": "Dimensionless" }, { "quantity": "Probability within one, two, and three standard deviations of the mean", "typical_range": "Approximately 68.27%, 95.45%, and 99.73%, respectively", "unit": "Percent" } ], "questions": [ { "id": "evidence-measurements-q01", "text": "Which of these typical measurements hold for the sense of normal distribution this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-failure-modes-and-hazards", "name": "Failure modes and hazards", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Applying a normal model to strongly skewed, multimodal, or heavy-tailed data can distort probabilities and uncertainty estimates.", "Normal tail estimates can substantially understate extreme-event probabilities when the actual distribution has heavier tails.", "Its unbounded support assigns probability to impossible values when used for strictly positive or bounded quantities.", "A central limit theorem concerns suitably normalised sums or averages under stated conditions; it does not establish that individual observations are normal.", "Marginal normality does not establish joint multivariate normality or independence." ], "questions": [ { "id": "evidence-failure-modes-and-hazards-q01", "text": "Which of these failure modes and hazards hold for the sense of normal distribution this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-neighbours", "name": "Neighbouring kinds and how to tell them apart", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ { "name": "Gaussian distribution", "difference": "A synonym for normal distribution, rather than a separate probability family." }, { "name": "Student's t-distribution", "difference": "Has heavier tails for finite degrees of freedom and approaches the standard normal distribution as the degrees of freedom increase." }, { "name": "Lognormal distribution", "difference": "A positive random variable is lognormal when its logarithm is normal; the variable itself is generally asymmetric." }, { "name": "Truncated normal distribution", "difference": "Conditions a normal variable to lie within a specified interval and renormalises its density." }, { "name": "Central limit theorem", "difference": "A class of convergence results explaining when normal limits arise, rather than a probability distribution." }, { "name": "Bell-shaped distribution", "difference": "A descriptive shape shared by several distributions; a normal distribution must satisfy the specific Gaussian density." } ], "questions": [ { "id": "evidence-neighbours-q01", "text": "Which of these neighbouring kinds and how to tell them apart hold for the sense of normal distribution this model covers, and on what evidence?", "kind": "provenance" } ] } ] } ] } ] }, "openQuestions": [ "Should this registry entry explicitly admit zero-variance Gaussian point masses, or reserve normal distribution for strictly positive variance?", "Which authoritative references and software interfaces should anchor the catalogue's parameter notation and precision conventions?", "What evidence and tolerances should an agent require before accepting a normal approximation for central probabilities versus extreme-tail decisions?", "Where should catalogue ownership fall between this family model and neighbouring models of normality diagnostics, parameter estimation, and sampling distributions?" ], "statistics": { "bundles": 6, "layers": 11, "findings": 17, "questions": 29 } }, "draft": { "generator": "vr.draft.v3", "status": "draft-generated", "researched": false, "archetype": "abstract concept", "method": "Written from the archetype playbook - what this kind of thing needs beyond identity and provenance - and from the structure that recurred across 6,333 models already researched by two engines. Applied to this entry by rule. No source was read for this thing and no claim here is researched. This entry carries no facets of its own, so they were inferred from its domain - a guess about a whole domain applied to one thing.", "facetsInferred": true, "nextPass": "A researcher replaces this draft with a sourced specification. Treat every sentence below as a proposal to argue with.", "purpose": "Give an agent a durable, checkable way to recognise a normal distribution, record what state it is in, and decide what may be done with it.", "whatItIs": "Enable an agent to identify a normal probability law, interpret its parameters, assess whether its use is justified, and perform probability calculations with explicit assumptions.", "characteristics": { "substance": "abstract", "origin": "conceptual", "agency": "inert" }, "whatYouCanDoWithIt": [ "observed and measured" ], "distinguishingFeatures": [ "Names folded into this entry, which a task may need to split apart again: standardized normal distribution.", "1 finer distinctions are held as aliases rather than separate entries, because telling them apart needs a task that asks for it.", "Described in 75 Wikipedia languages, which is a measure of how widely the thing is known, not of how important it is." ], "openQuestionsForResearch": [ "Which of the bundles below does a real task actually need, and which are ceremony?", "What does this thing have that the facets do not capture at all?", "Which neighbouring kind is most often confused with a normal distribution, and on what evidence are they told apart?" ], "whatItIsMadeOf": "an abstraction with no physical instance", "physicalCharacter": [ "Does nothing on its own; everything it does, something else did to it.", "These come from the domain this entry sits in rather than from the entry itself, so treat them as a first guess about the whole domain applied to one thing." ], "whatCanBeDoneWithIt": [ "observe it, measure it, record its state" ], "howItIsRecognised": [ "Nothing to see. What is recognised is an instance of it, and which instances count is exactly what is argued about." ], "relatedModels": [ { "relation": "covers", "note": "Finer kinds folded into this entry because telling them apart needs a task that asks for it. Each is a model waiting to be split out when one does.", "targets": [ "standardized normal distribution" ] } ], "standing": "Described in 75 Wikipedia languages, which measures how widely it is written about rather than how important or how common it is. 1 finer distinctions are held inside this entry as names rather than as separate models.", "structure": { "bundles": [ { "id": "identity-and-classification", "name": "Identity, naming and classification", "description": "How an agent tells one normal distribution from another, and a normal distribution from things that resemble it.", "rationale": "Recognition comes before every other claim. Without stable identity nothing else in the model can be trusted to be about the same thing twice.", "layers": [ { "id": "naming-and-identifiers", "name": "Names and identifiers", "description": "The names this thing goes by and the identifiers that survive translation and time.", "findings": [ { "id": "preferred-name-and-aliases", "name": "Preferred name, aliases and local names", "description": "Which name to use, which names mean the same thing, and which merely sound similar.", "questions": [ { "id": "preferred-name-and-aliases-q01", "text": "What identifies and describes the name of a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "preferred-name-and-aliases-q02", "text": "Who or what asserted this about the name of a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "preferred-name-and-aliases-q03", "text": "What may an agent decide or do once the name of a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "stable-identifiers", "name": "Stable identifiers and external keys", "description": "Identifiers that keep pointing at this kind of thing across systems and languages.", "questions": [ { "id": "stable-identifiers-q01", "text": "What identifies and describes an identifier for a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stable-identifiers-q02", "text": "Who or what asserted this about an identifier for a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stable-identifiers-q03", "text": "What may an agent decide or do once an identifier for a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "classification-and-granularity", "name": "Classification and granularity", "description": "Where a normal distribution sits among kinds, and how finely a task needs to cut it.", "findings": [ { "id": "kind-and-parents", "name": "Kind, parents and neighbouring kinds", "description": "The classes this thing belongs to and the ones it is next to.", "questions": [ { "id": "kind-and-parents-q01", "text": "What identifies and describes the kind of a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "kind-and-parents-q02", "text": "Who or what asserted this about the kind of a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "kind-and-parents-q03", "text": "What may an agent decide or do once the kind of a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "distinguishing-features", "name": "Distinguishing features", "description": "What separates a normal distribution from the things most often confused with it.", "questions": [ { "id": "distinguishing-features-q01", "text": "What identifies and describes what distinguishes a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "distinguishing-features-q02", "text": "Who or what asserted this about what distinguishes a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "distinguishing-features-q03", "text": "What may an agent decide or do once what distinguishes a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "state-and-lifecycle", "name": "State and lifecycle", "description": "The states a normal distribution passes through and the events that move it between them.", "rationale": "Most decisions about a thing depend on what state it is in now, which is a claim with a time on it, not a property.", "layers": [ { "id": "lifecycle-stages", "name": "Lifecycle stages", "description": "From coming into existence to ceasing to be one of these.", "findings": [ { "id": "stages-and-transitions", "name": "Stages and transitions", "description": "The stages worth naming and what moves a normal distribution between them.", "questions": [ { "id": "stages-and-transitions-q01", "text": "What identifies and describes the lifecycle of a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stages-and-transitions-q02", "text": "Who or what asserted this about the lifecycle of a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stages-and-transitions-q03", "text": "What may an agent decide or do once the lifecycle of a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "observations-and-status", "name": "Observations and current status", "description": "What is observed about a normal distribution, how often and by whom.", "findings": [ { "id": "observation-record", "name": "Observation record", "description": "How an observation of a normal distribution is recorded so that it can be superseded rather than overwritten.", "questions": [ { "id": "observation-record-q01", "text": "What identifies and describes an observation of a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "observation-record-q02", "text": "Who or what asserted this about an observation of a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "observation-record-q03", "text": "What may an agent decide or do once an observation of a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "definitions-in-contest", "name": "Definitions and who holds them", "description": "What normal distribution is taken to mean, and by whom.", "rationale": "When a field disagrees about a concept, the disagreement is the content. A model that picks one definition silently destroys the information.", "layers": [ { "id": "competing-definitions", "name": "Competing definitions", "description": "The main readings and the traditions behind them.", "findings": [ { "id": "definition-map", "name": "Definitions and their holders", "description": "Each definition with the school or body that holds it.", "questions": [ { "id": "definition-map-q01", "text": "Which definitions of normal distribution are in use, and which tradition or body holds each?", "kind": "definition" }, { "id": "definition-map-q02", "text": "What turns on the difference between them in practice?", "kind": "boundary" } ] } ] }, { "id": "operationalisation", "name": "Operationalisation", "description": "How it is measured or applied when it has to be.", "findings": [ { "id": "operational-record", "name": "Measures and proxies", "description": "Instruments and indicators used to stand in for it.", "questions": [ { "id": "operational-record-q01", "text": "How is normal distribution operationalised or measured in practice, and by what instrument?", "kind": "measurement" }, { "id": "operational-record-q02", "text": "What does that operationalisation leave out, and when does that matter?", "kind": "boundary" } ] } ] } ] }, { "id": "instances-and-use", "name": "Instances, use and consequence", "description": "What counts as an instance of normal distribution and what follows from calling something that.", "rationale": "Applying a concept is an act with consequences, so a model must say what the label licenses and what it does not.", "layers": [ { "id": "instances", "name": "What counts as an instance", "description": "Clear cases, borderline cases and non-cases.", "findings": [ { "id": "instance-tests", "name": "Tests for an instance", "description": "What would settle whether something falls under it.", "questions": [ { "id": "instance-tests-q01", "text": "What would settle whether something is an instance of normal distribution?", "kind": "boundary" }, { "id": "instance-tests-q02", "text": "Which borderline cases are argued about, and on what grounds?", "kind": "definition" } ] } ] }, { "id": "consequence", "name": "Consequence of application", "description": "Rights, duties or decisions that follow from the label.", "findings": [ { "id": "consequence-record", "name": "What the label licenses", "description": "What an agent may do once something is classified this way.", "questions": [ { "id": "consequence-record-q01", "text": "What follows practically once something is treated as normal distribution?", "kind": "action" }, { "id": "consequence-record-q02", "text": "What must an agent not infer from the label alone?", "kind": "action" } ] } ] } ] }, { "id": "provenance-and-evidence", "name": "Provenance, evidence and time", "description": "Where every claim about a normal distribution came from and when it held.", "rationale": "A claim without a source and a time cannot be superseded, only overwritten, and an agent that overwrites loses the ability to explain itself.", "layers": [ { "id": "source-and-authority", "name": "Source and authority", "description": "Who said it, on what evidence, and how strongly.", "findings": [ { "id": "claim-provenance", "name": "Claim provenance and confidence", "description": "The authority behind each claim about a normal distribution and how confident it is.", "questions": [ { "id": "claim-provenance-q01", "text": "What identifies and describes a claim about a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "claim-provenance-q02", "text": "Who or what asserted this about a claim about a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "claim-provenance-q03", "text": "What may an agent decide or do once a claim about a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "time-and-versions", "name": "Time, versions and supersession", "description": "When a claim was true, when it was learnt, and what replaced it.", "findings": [ { "id": "validity-and-supersession", "name": "Validity period and supersession", "description": "How an old claim about a normal distribution is retired without being erased.", "questions": [ { "id": "validity-and-supersession-q01", "text": "What identifies and describes the validity of a claim about a normal distribution, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "validity-and-supersession-q02", "text": "Who or what asserted this about the validity of a claim about a normal distribution, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "validity-and-supersession-q03", "text": "What may an agent decide or do once the validity of a claim about a normal distribution is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] } ] }, "statistics": { "bundles": 5, "layers": 10, "findings": 12, "questions": 32 } } } }