Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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 it