<b>GOALS FOR ACADEMIC WRITING: ESL STUDENTS AND THEIR INSTRUCTORS.</b><i>Alister Cumming (Ed.)</i>. Amsterdam: Benjamins, 2006. Pp. xii + 204. $42.95 paper.
Bibliographic record
Abstract
GOALS FOR ACADEMIC WRITING: ESL STUDENTS AND THEIR INSTRUCTORS.Alister Cumming (Ed.). Amsterdam: Benjamins, 2006. Pp. xii + 204. $42.95 paper. In universities throughout the world, faculty engage in discussions related to the writing performance of their students—with many concerned about challenges second language (L2) students face in achieving competence in academic writing tasks. Yet, most studies in the field of L2 writing focus on a single area of concern within the learning and teaching spectrum; the collective results of these explorations must often be pieced together by an individual who reviews a large number of separate studies. Cumming's edited volume stems from a more ambitious and data-rich approach; its chapters derive from a 2-year project in which the contributors investigated and compared the learning and teaching goals of several dozen L2 writers and a number of their teachers as the students moved from an intensive English as a second language (ESL) program in Canada to their first year at several Canadian universities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.038 |
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".