Bibliographic record
Abstract
Whatever we choose to do, the stakes are very high. David Whyte (1994 , p. 298), poet Researching questions that matter demands passionate conviction. Whether recognized as such or not, such conviction, combined with profound compassion, defines true scholarship. Daring to care requires courage—the courage to speak out and to act. Courage transforms convictions and compassion into action. Thus, by its very nature, daring to care calls into question the traditional role of rigid scientific objectivity and invites advocacy to play a vital role within our scholarly tradition. In focusing on daring to care, this article raises questions that academia must ask itself in order to support scholars in rigorously researching and teaching about issues that matter. It provides examples of scholarship that have required courage, conviction, and compassion, including a case example where the outcome of appropriate methodology is literally life or death. Throughout the discussion, readers are invited to consider what supports their core convictions, compassion, and courageous action in their own scholarship, teaching, and advocacy.
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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 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".