Migratory Legend vs Fairy Tale Classifier
Audit independently established presentation/context, not truth or automatic legend detection. Independently annotated feature columns in order: story presented as situated credible occurrence, marvelous tale presented as fiction. Compatible classes are not established identities; unknown evidence retained. No automatic semantic parsing, diagnosis, authenticity or safety determination.
Description
A legend may be presented as a situated credible occurrence, while a fairy tale may be framed as marvelous fiction.
A legend may be presented as a situated credible occurrence, while a fairy tale may be framed as marvelous fiction.1
Those independently annotated presentation features support a limited comparison without deciding whether an event actually happened. Use this tool to compare independently annotated features with the stated candidate rules.
When to use Migratory Legend vs Fairy Tale 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 Migratory Legend vs Fairy Tale 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 | story presented as situated credible occurrence |
| 1 | marvelous tale presented as fiction |
| Candidate | story presented as situated credible occurrence | marvelous tale presented as fiction |
|---|---|---|
| legend-candidate | Known present (1) | Known absent (0) |
| fairy-tale-candidate | Known absent (0) | 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 values |
| result.witnessedClasses | Every fixed candidate with all nonignored constraints actually known and matched | list of values |
| result.unknownFeatureIndices | Zero-based indices of unknown feature observations | list of values |
{
"input": {
"observedFeatureFlags": [
1,
1
],
"featureKnownFlags": [
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [],
"witnessedClasses": [],
"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 Migratory Legend Type ML Number Lookup for this related task: Migratory-legend classification uses type numbers tied to an adopted scholarly catalog.
Use Thompson Motif Index Code Lookup when its separate inputs and assumptions fit the question: A motif index organizes recurring narrative elements under an edition-specific classification scheme.
References
Similar or alternative tools
- Migratory Legend Type ML Number Lookup
Migratory-legend classification uses type numbers tied to an adopted scholarly catalog.
- Thompson Motif Index Code Lookup
A motif index organizes recurring narrative elements under an edition-specific classification scheme.