Bibliographic record
Abstract
Research training is challenging, and the attrition rate of doctoral students has been increasing in Canada, the UK, the USA and Australia. In their book, Chen and Le examine the reasons for these students becoming demotivated, particularly in the context of TESOL. There has been much investigation into research training issues in multiple contexts and multiple disciplines. Yet, the research training process in TESOL for international students has not been explored sufficiently, and their voices have not been heard. This book gives voice to the research trainees, allowing their experiences to be reflected and the implications discussed in order to help create more effective supervision models. By employing the qualitative approach and adopting critical incident as a new technique for data collection, Chen and Le attempt to gain insights into the research training process to reveal different research stages of research trainees—those undertaking PhD degrees—and to put forward a model of supervision to improve the innovation and quality of research. This book tackles the complex nature of research training. It is hoped that findings of this study can provide research supervisors and trainees with theoretical insights and practical references.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.012 |
| 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".