R-Dropping Dialect Classifier

Screen explicitly observed r retention by annotated prevocalic versus nonprevocalic contexts, preserving missing-context evidence; no region/dialect assignment, linking-r/intrusive-r modeling or audio diagnosis.

Description

Observed r retention depends on phonological context.

Observed r retention depends on phonological context. Missing contexts cannot establish a complete nonrhotic pattern.^[1]

Enter aligned nonempty integer context codes 0prevocalic/1preconsonantal/2final and binary observed retention. Pool1/2 as nonprevocalic; return group percentages or −1 when absent. Match requires both groups, all prevocalic retained and all others dropped.

When to use R-Dropping Dialect Classifier

Use R-Dropping Dialect Classifier when you need to evaluate the supplied records under the calculation and evidence rules below. No region, dialect, linking/intrusive r or pronunciation from audio is inferred.

Prepare explicit text tokens, annotated language observations, caller mappings or a supported locale as requested. Preserve source evidence and the stated Unicode counting convention.

How to use R-Dropping Dialect Classifier and understand its results

Prepare explicit text tokens, annotated language observations, caller mappings or a supported locale as requested. Preserve source evidence and the stated Unicode counting convention.
Enter callerRContextsPrevocalicPreconsonantalFinal, observedRRetainedFlags using the field table and calculation rules, then run the tool.
Read prevocalicObservedRetentionPercent, nonPrevocalicObservedRetentionPercent, bothContextGroupsObserved, matchesObservedNonrhoticPattern. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
callerRContextsPrevocalicPreconsonantalFinal	Caller r contexts prevocalic preconsonantal final	list of number	Required
observedRRetainedFlags	Observed r retained flags	list of number	Required

retention%=100×retained/count per context group

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
prevocalicObservedRetentionPercent	Prevocalic observed retention percent	number
nonPrevocalicObservedRetentionPercent	Non prevocalic observed retention percent	number
bothContextGroupsObserved	Both context groups observed	boolean
matchesObservedNonrhoticPattern	Matches observed nonrhotic pattern	boolean

json

{
  "input": {
    "callerRContextsPrevocalicPreconsonantalFinal": [
      0,
      1,
      2
    ],
    "observedRRetainedFlags": [
      1,
      0,
      0
    ]
  },
  "output": {
    "prevocalicObservedRetentionPercent": 100,
    "nonPrevocalicObservedRetentionPercent": 0,
    "bothContextGroupsObserved": true,
    "matchesObservedNonrhoticPattern": true
  }
}

Limitations and assumptions

No region, dialect, linking/intrusive r or pronunciation from audio is inferred.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Numeric lists are limited to 10,000 entries; string lists to 128 strings of at most 128 characters each. The rules above may impose smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors. A successfully returned false flag is an evaluated outcome; its meaning is explained above.

Alternative or Complementary methods

Use Castilian Distinción Theta Check: Distinción is screened from observed s versus c/z lexical sets and actual realizations. Both sets are needed to support a distinction pattern. For a complementary task, use Mutual Intelligibility Percent Pair Lookup: Comprehension can differ by testing direction. Pooling observed words weights directions by their actual test sizes.

References

R-Dropping Dialect Classifier — tool.io

Similar or alternative tools

Castilian Distinción Theta Check
Distinción is screened from observed s versus c/z lexical sets and actual realizations. Both sets are needed to support a distinction pattern.
Mutual Intelligibility Percent Pair Lookup
Comprehension can differ by testing direction. Pooling observed words weights directions by their actual test sizes.