Learning Transfer or Transforming Learning?: Student Interns Reinventing Expert Writing Practices in the Workplace
Bibliographic record
Abstract
Graham Smart and Nicole Brown L'article rend compte d'une etude qualitative portant sur les experiences de 24 Ctudiants de premier cycle inscrits a une majeure en redaction, au moment de leur entree dans le monde du travail.En exerrant des taches de redaction dans une variete de genres discursifs, ces internes ont mis en a:uvre et approfondi des pratiques d'ecriture d'experts par le biais de leurs interactions avec leurs collegues de travail et d'artefacts culturellement construits.Remettant en question la conception cognitiviste du transfert des connaissances, l'etude suggere que la transformation de l'apprentissage a pennis la reinvention des pratiques des experts.L'ctude presente aussi une variante du modele d'acquisition du savoir en situation de dcbutant en decrivant comment Les internes ont compense leur manque de savoir-faire sur le terrain par l'acces aux elements cognitifs inherents awe artefacts culturels.When I started, I was just a student intern; my name was Martha Smith, JAI-"Just an Intern."... [But] by the end, it was different-it was like being ... a real tech writer.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".