The syntax of African American English borrowings in the Louisiana Creole tense-mood-aspect system
Bibliographic record
Abstract
Abstract This paper presents the typologically unusual case of borrowed tense-mood-aspect morphemes. Data are taken from Louisiana Creole, a critically endangered French-lexifier creole. Over the course of its history, Louisiana Creole has been in contact with local varieties of both French and English, including African American English. It will be shown that points of structural congruity between Louisiana Creole and African American English have facilitated the borrowing of two aspect markers for speakers competent in both varieties. African American English stressed BIN has been borrowed and marks remote past habitual, stative, and completive. The adverb still has been borrowed and subsequently has grammaticalized as a continuative marker via spec-to-head reanalysis. These borrowings are integrated into the inflectional domain as functional heads marking aspect. Their ordering constraints are evaluated relative to a previous hierarchy proposed by Rottet. Discussion of contact-induced change in creole languages has typically been confined to examination of interactions with the lexifier, the language which contributes the majority of a creole’s vocabulary (in this case, French). Fewer studies have presented detailed accounts of how creoles behave when in contact with other languages, meaning that this particular contact context remains undertheorized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".