Raising the profile of Learning Development: thinking forwards
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
Following an article comparing ‘study skills’ provision to J.M. Barrie’s Tinkerbell (Richards and Pilcher, 2023), discussions – and emotions – were stirred again this year regarding how Learning Development is understood among academic and third-space colleagues. Inspired by White and Webster’s (2023) session at last year’s ALDcon, the Study Advice service at the University of Reading, in collaboration with Dr Helen Webster from the University of Oxford, decided to run an ALDinHE regional event to address this very question: how do Learning Developers promote a better understanding of what we do, and raise our profile in our institutions? This presentation reported back from this regional event, sharing both the barriers and proposed solutions to raising our profile. But there is still work to do. Together we hope to create a useful action plan from this work. We discussed whom we need to communicate with, what we feel these messages should be, and how we claim our expertise. Finally, we considered what we can do as a cross-institutional collective to ensure that we are seen as a profession with our own expertise and identity.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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