Book Review: Contemporary Language Motivation Theory – 60 Years Since Gardner and Lambert (1959) by Ali H. Al-Hoorie and Peter D. MacIntyre
Bibliographic record
Abstract
Whether it is a truism or a cliché, we stand on the shoulders of giants. Professor Robert Gardner is certainly one of them. The year 2019 marked 60 years since the publication of his seminal paper, Motivational Variables in Second Language Acquisition, written together with Wallace E. Lambert and published in the Canadian Journal of Psychology in 1959. The paper is widely acknowledged as the beginning of L2 motivation research, and to borrow Dörnyei’s words introducing this anthology, “[it] is not only important because it was a historical milestone and offered fertile ground in which subsequent research could grow, but also because it is still relevant” (Dörnyei, 2020, p. xxi). Having Dörnyei’s blessing, and with the caliber of contributors in this anthology, the relevance of Gardner’s legacy of research is without question. In this festschrift, editors Ali H. Al-Hoorie and Peter D. MacIntyre bring together twenty-six scholars in the fields of applied linguistics, social psychology, and sociology to honor the career and research contributions of Robert Gardner. Each contribution connects to, develops, and builds upon the ideas of Gardner and Lambert, and links them to current trends and developments related to contemporary language motivation research. Thus, this collection of papers will delight and catch the attention of a wide readership from graduate students to practitioners to established researchers alike.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".