On Determinatives and the Category-Function Distinction: A Reply to Brett Reynolds
Bibliographic record
Abstract
This article examines the arguments made in the article “Determiners, Feline Marsupials, and the Category-Function Distinction: A Critique of ELT Gram- mars” by Brett Reynolds recently published in the TESL Canada Journal (2013). In our response, we demonstrate that the author’s arguments are problematic on both theoretical and empirical grounds. In particular, we show that, by the author’s own metrics, (a) the items in the so-called my set (i.e,. my, your, his/ her, etc.) should be determinatives rather than pronouns, and (b) even items that the author argues to be determinatives (i.e., all, many, few, little, etc.) cannot be classified as such if we apply the tests suggested by the author. We conclude our critical response by discussing some of the pedagogical implications of the author’s article.Cet article porte sur les arguments présentés dans l’article “Determiners, Feline Marsupials, and the Category-Function Distinction: A Critique of ELT Gram- mars” écrit par Brett Reynolds et récemment publié dans la Revue TESL du Ca- nada (2013). En réponse à cet article, nous démontrons que les arguments de l’auteur sont problématiques sur les plans tant théorique qu’empirique. Plus pré- cisément, nous expliquons, en nous basant sur les paramètres mêmes de l’auteur, que (a) les items de l’ensemble qu’il nomme ‘my set’ (c.-à-d., ma, ta, sa, etc.) devraient être considérés des déterminants plutôt que des pronoms et que (b) même des items que l’auteur décrit comme étant des déterminants (c.-à-d., tout, plusieurs, peu, etc.) ne peuvent être classés ainsi si l’on se base sur les tests qu’il propose. Nous concluons notre critique en discutant certaines incidences pédago- giques découlant de l’article de l’auteur.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".