Pidgin vs Creole Classifier

Caller independently documented transmission/community/functional-domain evidence for pidgin/creole candidate criteria, not a social label inferred from simplicity. Independently declared positive requirements, overlapping/unknown classes retained. Caller establishes language, dialect, method and reference context; no automatic identity or universal language-wide claim.

Description

Pidgin and creole classifications involve contact history and language use as well as structural descriptions.

Pidgin and creole classifications involve contact history and language use as well as structural descriptions.^[1]

Positive evidence must be interpreted under an explicitly adopted classification scheme. The tool compares your observed and known flags with your declared positive requirements, retaining unresolved candidates.

When to use Pidgin vs Creole Classifier

Use this tool to calculate or review the supplied observations under the stated rules. Specify the language variety, analysis, source edition and population or inventory definition.

How to use Pidgin vs Creole Classifier and understand its results

Specify the language variety, analysis, source edition and population or inventory definition.
Enter callerContactVarietyClassIds, callerContactVarietyRequirements, observedContactVarietyEvidence, knownContactVarietyFlags, using the field meanings and rules below, then run the tool.
Read compatibleCallerClasses, fullyWitnessedCallerClasses, using the output meanings and calculation rules below. Distinguish compatible candidates from fully witnessed classes; compatibility can retain unknown required evidence.
Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.

Input field	Meaning	Value type	Requirement
callerContactVarietyClassIds	Caller contact variety class ids	list of string	Required
callerContactVarietyRequirements	Caller contact variety requirements	numeric matrix	Required
observedContactVarietyEvidence	Observed contact variety evidence	list of number	Required
knownContactVarietyFlags	Known contact variety flags; binary 1 means the matched observation is known, 0 means unknown	list of number	Required

Use unique nonblank class IDs and a nonempty binary requirement matrix aligned by class and evidence column. Every class must require at least one positive feature. A one requires presence; zero ignores the feature and does not require absence. Supply binary observed evidence and known flags. A known required zero excludes a class; an unknown required feature retains compatibility but prevents full witnessing. Return all compatible classes and the subset with every required feature known and present. Classes may overlap.

compatible_c=∀j(requirement_cj=0 or known_j=0 or evidence_j=1); witnessed_c=∀j(requirement_cj=0 or (known_j=1 and evidence_j=1))

Use the matching field units and the exact boundary, ordering and reference rules above.

Output field	Meaning	Value type
compatibleCallerClasses	Compatible caller classes	list of string
fullyWitnessedCallerClasses	Fully witnessed caller classes	list of string

json

{
  "input": {
    "callerContactVarietyClassIds": [
      "a",
      "b"
    ],
    "callerContactVarietyRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedContactVarietyEvidence": [
      1,
      0
    ],
    "knownContactVarietyFlags": [
      1,
      0
    ]
  },
  "output": {
    "compatibleCallerClasses": [
      "a",
      "b"
    ],
    "fullyWitnessedCallerClasses": [
      "a"
    ]
  }
}

Limitations and assumptions

The language variety, analytical definitions and reference scope are supplied by the caller. No corpus, speech or complete/current linguistic registry is analyzed automatically. Compatible classes with unknown evidence are not positive identifications. Fully witnessed classes satisfy only the declared positive requirements.

Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above.

Alternative or Complementary methods

For a related step, use Creole Origin Pair Lookup: Creole-origin references describe particular contact histories and contributing languages. For a complementary comparison, use Cognate vs Loanword Classifier: Cognates and loans involve different historical relationships between forms.

References

Pidgin vs Creole Classifier — tool.io

Similar or alternative tools

Creole Origin Pair Lookup
Creole-origin references describe particular contact histories and contributing languages.
Cognate vs Loanword Classifier
Cognates and loans involve different historical relationships between forms.