Bibliographic record
Abstract
New theories are a constant of the now vast literature on code-mixing (CM). The Gradient Symbolic Computation model proposed by Goldrick, Putnam and Schwartz (Goldrick, Putnam & Schwartz) will appeal to many, especially those who already espouse constraint-based approaches to grammar. As variationist sociolinguists, we particularly welcome the model's incorporation of “relative probabilities of certain structures”, a feature we believe can enhance our chances of capturing actual CM behavior. We also applaud Goldrick et al.’s efforts to integrate experimental findings on co-activation with grammatical principles. Our questions concern the utility of “doubling constructions” to showcase the model, and by extension, the degree to which it can account for bilinguals’ spontaneous production of CM. A historical perspective on the field shows that none of the myriad theories of CM, often inspired by competing sets of grammatical principles, has yet achieved broad acceptance. In the absence of any widely endorsed evaluation metric – still sadly lacking -– how are we to decide amongst them?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.205 | 0.091 |
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".