Bibliographic record
Abstract
The present paper investigates one aspect of questionable research practices relating to Arabic L1 learners of foreign languages, namely the use of statistics. The objective of the paper is to argue that reproducible research requires adopting wise practices in linguistics and that the excessive focus on quantification does not seem to serve this purpose. Statistical significance tests in quantitative research are routinely used in linguistic inquiry as well as language teaching and learning studies with a view to supporting the relevant explanatory insights in linguistics. In this article, I will expose the misuse of statistics by doctoral students in English departments of Morocco working on Arabic L1 learners’ data, by highlighting some practices that are at odds with international good practices in academic research in linguistics. I will take stock of the current questionable practices in this regard to dispel some of the misunderstanding about the use of statistics which is now gaining grounds lest this becomes an orthodoxy. I will argue that research on Arabic L1 learners’ data should be focused more on exploration and discovery, as well as the validation of epistemological insights than on mere descriptive quantification geared to hypothesis verification. These areas of focus constitute the crux of academic research in linguistics, but they seem to be lost in statistics in doctoral students’ theses. Recommendations and solutions are provided for enhancing transparency and improving reproducibility of doctoral research outcomes to advance theory building and the delivery of new research lines in linguistics as well as to avoid the risk of research waste, in line with the requirements of open science.
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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 | 0.210 | 0.559 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.009 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".