A Practice-Oriented Definition of Post-Process Second Language Writing Theory
Bibliographic record
Abstract
This article is a synthesis of the scholarly literature on the post-process approach to teaching second language (L2) writing, particularly college and university composition in English as an additional language. This synthesis aims to offer a definition of post-process L2 writing that can readily lend itself to practice and be more accessible to practitioners. All the publications that had either substantially or marginally discussed post-process theory since 1990 were systematically reviewed in order to answer the following question: What is a definition of post-process L2 writing theory that can readily lend itself to pedagogy and actual practice for helping college and university writers of English as an additional language?Cet article est une synthèse de la littérature savante sur la méthode post-processus de l'enseignement de la rédaction en langue seconde (L2), notamment de l'écriture dans les cours d'anglais langue additionnelle dans les collèges et les universités. L'objectif de cette synthèse est de proposer une définition de la rédaction post-processus en L2 qui puisse se prêter facilement à la pratique et être plus accessible aux praticiens. On a examiné systématiquement toutes les publications ayant porté, ou même évoqué, la théorie du post-processus depuis 1990 et ce, de sorte à répondre à la question suivante : Quelle définition de la rédaction post-processus en L2 peut facilement se prêter aux fins pédagogiques et pratiques dans les cours d'anglais langue additionnelle dans les collèges et les universités?
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".