“When I went to Canada, I saw the madre" : evaluating two theories' predictions about codeswitching between determiners and nouns using Spanish-English and Welsh-English bilingual corpora
Bibliographic record
Abstract
Previous work on intrasentential codeswitching has noted that switches between determiners and their noun complements are frequent in both SpanishEnglish and WelshEnglish data. Two major recent theories of codeswitching, the Matrix Language Frame model and a Minimalist Program approach, make potentially competing predictions regarding the source language of the determiner in these mixed nominal constructions. In this paper we evaluate the predictions of each theory with reference to comparable sets of SpanishEnglish and WelshEnglish codeswitching data. Mixed nominal constructions are extracted to test the compatibility of these data with the predictions, taking into account coverage and accuracy. We find that the data are broadly consistent with each set of predictions but do not find statistically significant differences between the accuracy of the predictions of the two theories.We examine in detail the counterexamples to the predictions of each theory to see what further factors may influence codeswitching patterns between determiners and their nouns, and also discuss the differences in observed patterns in the data from each language pair.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".