Historical Geographic Method Monogenesis Classifier

Separate independently established historical-geographic variant evidence from origin inference. Independently annotated feature columns in order: independent variants mapped, single-origin evidence independently supported, multiple-origin evidence independently supported. Compatible classes are not established identities; unknown evidence retained. No automatic semantic parsing, diagnosis, authenticity or safety determination.

Description

The historical-geographic method compares recorded tale variants and their distribution.

The historical-geographic method compares recorded tale variants and their distribution.^[1]

Evidence compatible with a common origin is distinct from proving transmission or excluding independent development. Use this tool to compare independently annotated features with the stated candidate rules.

When to use Historical Geographic Method Monogenesis Classifier

Use this tool to compare independently annotated features with the stated candidate rules. Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.

How to use Historical Geographic Method Monogenesis Classifier and understand its results

Identify the community, narrative version, source edition and interpretation context; prepare any annotations independently.
Enter observedFeatureFlags, featureKnownFlags, using the field meanings and rules below, then run the tool.
Read result.compatibleClasses, result.witnessedClasses, result.unknownFeatureIndices. Interpret the fields using their units, signs and evidence conventions below.
If input validation fails, correct the reported field and run again. Before applying the result, check reference scope, model assumptions and any missing evidence or placeholder values.

Input field	Meaning	Value type	Requirement
observedFeatureFlags	Observed feature flags; binary 0/1 under the definitions below	list of number	Required
featureKnownFlags	Feature known flags; binary 1=known, 0=unknown, in the order described below	list of number	Required

Supply binary observation and binary known flags in the fixed column order below. Rule −1 ignores a feature; rule 0 requires known absence and rule 1 requires known presence for a witnessed match. A compatible match permits unknown required features but rejects conflicting known observations. Return every compatible candidate, every fully witnessed candidate and zero-based unknown indices. All-unknown evidence can leave every candidate compatible while witnessing none; incompatible evidence may return no candidates. There is no automatic text interpretation or judgment of truth, authenticity or narrative quality.

Zero-based column	Observed feature
0	independent variants mapped
1	single-origin evidence independently supported
2	multiple-origin evidence independently supported

Candidate	independent variants mapped	single-origin evidence independently supported	multiple-origin evidence independently supported
monogenesis-evidence-candidate	Known present (1)	Known present (1)	Ignored
polygenesis-evidence-candidate	Known present (1)	Ignored	Known present (1)

compatible={candidate:all constraints ignored, unknown or matched}; witnessed={candidate:all nonignored constraints known and matched}; unknown={i:known_i=0}

Use the exact units, boundary rules, ordering and missing-evidence conventions above.

Output field	Meaning	Value type
result	Structured calculation or selected reference, with fields listed below	object
result.compatibleClasses	Every fixed candidate whose rules do not conflict with known features	list of string
result.witnessedClasses	Every fixed candidate with all nonignored constraints actually known and matched	list of string
result.unknownFeatureIndices	Zero-based indices of unknown feature observations	list of values

json

{
  "input": {
    "observedFeatureFlags": [
      1,
      1,
      1
    ],
    "featureKnownFlags": [
      1,
      1,
      1
    ]
  },
  "output": {
    "result": {
      "compatibleClasses": [
        "monogenesis-evidence-candidate",
        "polygenesis-evidence-candidate"
      ],
      "witnessedClasses": [
        "monogenesis-evidence-candidate",
        "polygenesis-evidence-candidate"
      ],
      "unknownFeatureIndices": []
    }
  }
}

Limitations and assumptions

Features and criteria are independent annotations. Compatible candidates and retained unknown evidence do not establish an identity, interpretation, overall pass or permission.

All listed fields require explicit values. Unexpected fields, incorrect types, nonfinite numbers and sparse evidence arrays fail validation; the specific bounds and alignment requirements are stated above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful result reporting false, no matches or missing evidence.

Alternative or Complementary methods

Use Etiological Origin Myth Type Classifier for this related task: An etiological narrative explains why a feature, place or custom has its present form.

Use ATU Tale Type Lookup when its separate inputs and assumptions fit the question: The ATU system groups folktales into editorially defined tale types.

References

Historical Geographic Method Monogenesis Classifier — tool.io

Similar or alternative tools

Etiological Origin Myth Type Classifier
An etiological narrative explains why a feature, place or custom has its present form.
ATU Tale Type Lookup
The ATU system groups folktales into editorially defined tale types.