Determiners, Feline Marsupials, and the Category-Function Distinction: A Critique of ELT Grammars
Bibliographic record
Abstract
The concept of determiners is widely employed in linguistics, but mostly absent from English Language Teaching (ELT) materials (dictionaries, teacher-reference books, and student-oriented texts). Among those employing the concept, there is near-universal confusion between determiners and pronouns, arising mainly from an analytical and terminological failure to distinguish consistently between the category (determinative) and the function (specifier). I criticize this situ- ation and present linguistic evidence for a more consistent framework. I conclude by arguing that in language teaching and applied linguistics we rarely adopt advances from linguistics, not because they fail to meet some criterion of rele- vance à la Widdowson (2000), but simply because we are ignorant of linguistics in general.Le concept de déterminants s’emploie largement en linguistique, mais il est très peu présent dans le matériel pédagogique pour l’enseignement de l’anglais (dic- tionnaires, manuels de référence pour les enseignants, manuels pour les étudi- ants). Parmi ceux et celles qui emploient le concept, il existe une confusion quasi universelle entre les déterminants et les pronoms. Cette confusion découle no- tamment d’une analyse erronée et d’une erreur terminologique faisant en sorte qu’on ne distingue pas toujours la catégorie (déterminant) de la fonction (spé- cificateur). Je critique cette situation et présente des données linguistiques qui plaident en faveur d’un cadre plus constant. Je conclus en affirmant qu’en en- seignement des langues et en linguistique appliquée, nous adoptons rarement les avancées du domaine de la linguistique, pas parce qu’elles ne répondent pas à des critères de pertinence à la Widdowson (2000), mais parce que nous connaissons mal la linguistique de façon générale.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| 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".