<b>INDIVIDUAL DIFFERENCES IN FOREIGN LANGUAGE LEARNING: EFFECTS OF APTITUDE, INTELLIGENCE, AND MOTIVATION.</b><i>Steve Cornwell and Peter Robinson (Eds.)</i>. Tokyo: Aoyama Gakuin University, 2000. Pp. ii + 199. $29.00 paper.
Bibliographic record
Abstract
This edited volume contains the proceedings of a conference on the role of individual differences in instructed SLA held at Aoyama Gakuin University in 1999. It includes 11 studies as well as an introductory chapter written by the editors. The first two papers, by Yamashiro and McLaughlin and by Hiser, Croker, Kenudson, and Stribling, present multivariate statistical analyses to examine the interrelationship between motivation, second language (L2) proficiency, and other learner characteristics in Japanese student samples. Although both studies offer unique insights into the characteristics of their target population, the authors also call for methodological improvements that would go beyond the use of self-report questionnaires. Sharing a similar interest in improving motivation research methodology, another study by Hsiao explicitly sets out to test the construct validity of the most well known motivation test—Gardner's Attitude/Motivation Test Battery—in Taiwan, which is a very different learning environment from Canada, where the test was originally developed. Özek and Williams's paper is also of interest in this respect because in their study a questionnaire survey conducted in Turkey was complemented with qualitative interviews, thereby resulting in a particularly rich database.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".