Bibliographic record
Abstract
Issue 1 of Volume 14 is published at an exciting and challenging time for education. The availability of generative artificial intelligence (AI) tools is causing disruption across sectors. Responses range from complete bans in some compulsory education and tertiary institutions, to putting in place creative ways to deploy these new technologies as productive learning and work tools. The concerns about the risks to integrity of assessment and reputational risks to institutions and sectors are valid and also require close attention. In a short time, a lot of advice has been offered and forums discussing approaches to integrating generative AI into work as well as assessment practices abound. The Student Success team has been watching these developments with great interest. We believe these tools have utility for both learning practice and helping build students’ capacity to succeed. We look forward to receiving evidence-based submissions on this important topic for future issues. In this general issue we present a broad spectrum of articles and practice reports on student engagement, this time with authors from Australia, South Africa, Canada and the US.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.167 | 0.097 |
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".