Does Academic Research Make A Difference with Dr. Lissa Paul
Bibliographic record
Abstract
Jennifer Slagus and Josh Palange interview Dr. Lissa Paul, a professor at Brock University in Ontario, Canada. Throughout the episode they emphasize the need for accessible and inclusive scholarly research, highlighting the importance of open access, public engagement, and representing diverse perspectives. Dr. Lissa Paul provides insight into her own work as an academic and the ways in which her projects, such as her work with the British Library Endangered Archives Programme, has benefited the broader community. She also gives a brief overview on the historical development of the children’s book publishing industry and discusses the Barnes & Noble Diverse Editions controversy with Jennifer Slagus and Josh Palange.
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 | 0.062 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.033 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".