Putting The Heart Back Into Publishing
Bibliographic record
Abstract
This panel of senior scholars will focus on what it means to have heart in publishing. They will do this through their individual stories. The panellist will highlight the challenges scholars face today with the publish and perish dilemma and also recommend what should be done, drawing on their global experiences in management, consulting, teaching, journal editors and research scholars. We will deliberate the new trends (predatory journals, paper mills, platform survey sites, big data, and AI) that threaten the foundation on what it means to be published. This panel hopes that through their deliberations, and through sharing of knowledge we can return back to the heart of research – the love of knowledge and the quest to produce something we can use to solve problems that may help make a better world. We welcome researchers from all disciplines to listen and to share their stories of how manage to publish with heart.
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 | 0.037 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.061 | 0.052 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".