The LearnHigher Resource-athon! Creative, collective contributions to the LearnHigher resource bank
Bibliographic record
Abstract
In this creative, participatory workshop, colleagues had the opportunity to contribute directly to the LearnHigher resource bank. The premise was to bring together colleagues from across the sector to work together in cross-institutional teams to create new resources for LearnHigher. Colleagues benefitted from networking opportunities, as well as a chance to have their work published and disseminated online. As part of the session, participants were provided with prompts arising from the conference’s key themes – they were encouraged to consider disciplinarity/cross-disciplinary approaches, inclusivity, research-based practice and technologies for learning. The session concluded with information about the next steps in the LearnHigher resource review process and an opportunity to put any questions to members of the LearnHigher working group. Each group was supported and mentored by a member of the LearnHigher working group, who offered advice and prompts for resource development. Since the conference, colleagues have received continued support from LearnHigher mentors, as the resources are being refined, finalised and submitted for peer review, prior to publication on the LearnHigher website.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.096 | 0.024 |
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".