Bibliographic record
Abstract
ABSTRACT. What is second language acquisition like from the learner’s perspective? I examined published autobiographies authored by those who have documented their language learning journeys. One theme that recurred across the texts was Writing; a sub-theme was Writing life 1 in language 2. Some narrativists/learners described the dissonance, while others described the relief they felt when writing about events in a language other than the language in which those events occurred. Insights about writing provided by the learners/narrativists could illuminate both second language acquisition (SLA) theory and SLA pedagogy. ECRIRE LA VIE 1 DANS LA LANGUE 2 RESUME. Comment l’apprenant considere-t-il l’acquisition d’une langue seconde? J’ai examine des autobiographies publiees par des personnes qui ont documente leur parcours d’apprentissage d’une langue. L’un des themes recurrents des textes est l’ecriture; un sous-theme etant l’ecriture d’une vie dans une autre langue que la langue maternelle. Certains narrateurs/apprenants ont fait etat d’une discordance, tandis que d’autres ont exprime leur soulagement d’ecrire sur leur vie dans une langue autre que celle dans laquelle cette derniere s’est produite. Les observations que nous ont fournies les narrateurs/apprenants sur l’ecriture pourraient contribuer a enrichir la theorie de l’acquisition d’une langue seconde (SLA) et la pedagogie SLA.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".