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Record W4412847291 · doi:10.18806/tesl.v42i1/1426

The Grammar Puzzle

2025· article· en· W4412847291 on OpenAlexaffvenue
Mehrdad Yousefpoori-Naeim

Bibliographic record

VenueTESL Canada Journal · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLinguisticsGrammarPsychologyPhilosophy

Abstract

fetched live from OpenAlex

The role of grammar in reading comprehension remains unresolved despite the relatively large number of studies in this line of research. Drawing on the (dis)advantages of applying grammatical knowledge to the reading process, the present study discusses some of the reasons for differing results reported by previous studies. It is argued that grammatical knowledge can affect comprehension differently under different conditions. Specifically, grammar can be helpful in understanding complex sentences, while being even counterproductive when applied to texts with simpler structures. It is, therefore, important to take into consideration the interaction between grammatical knowledge and the text when examining the role of grammar in reading comprehension. An operational framework of grammatical knowledge with regard to reading comprehension is put forward to illuminate its role in reading comprehension and provide guidelines for teaching practice. The operational framework makes a primary distinction betweengrammatical knowledge and grammar instruction and further places them on the implicit/explicit continuum. It is concluded that knowing how and when to resort to grammar is as important for students as being competent in grammar. This conclusion highlights the significance of adopting a strategy-based approach to grammar instruction. Teachers should teach both cognitive and metacognitive strategies when teaching grammar for reading. Le rôle de la grammaire dans la compréhension de la lecture reste non résolu malgré le nombre relativement important d’études dans ce domaine de recherche. En s’appuyant sur les (dés)avantages de l’application des connaissances grammaticales au processus de lecture, la présente étude explore certains éléments qui expliquent les résultats divergents rapportés par les études précédentes. Elle soutient que les connaissances grammaticales peuvent affecter la compréhension différemment selon les conditions. Plus précisément, la grammaire peut être utile pour comprendre des phrases complexes, tout en étant contre-productive lorsqu’elle est appliquée à des textes ayant des structures plus simples. Il est donc important de prendre en considération l’interaction entre les connaissances grammaticales et le texte lorsque l’on examine le rôle de la grammaire dans la compréhension de la lecture. Un cadre opérationnel de la connaissance grammaticale en matière de compréhension de la lecture est proposé pour éclairer son rôle dans lacompréhension de la lecture et fournir des lignes directrices pour l’enseignement. Le cadre opérationnel établit une distinction primaire entre la connaissance grammaticale et l’enseignement de la grammaire et les place sur le continuum implicite/explicite. L’article conclut en avançant que savoir comment et quand recourir à la grammaire est aussi important pour les apprenants que d’être compétent en grammaire. Cette conclusion souligne l’importance d’adopter une approche basée sur les stratégies pour l’enseignement de la grammaire. Les enseignants devraient enseigner des stratégies cognitives et métacognitives lorsqu’ils enseignent la grammaire pour la lecture.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.014
Scholarly communication0.0050.010
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0230.005

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.

Opus teacher head0.010
GPT teacher head0.304
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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