Keynote: When Good Intentions Just Aren’t Enough: Engaging Diverse Communities as Partners in Knowledge
Bibliographic record
Abstract
SPEAKER: Alpha Abebe, PhD Assistant Professor, Communication Studies & Media Arts, Faculty of Humanities McMaster University REPORTER: Peter J Olson JAMA Network A fundamental aspect of the scientific enterprise is that it begins with a question about our world and the way it works. What comes next is extensive, laborious research that may or may not yield satisfactory answers, and there is always more work to be done to convert newly acquired knowledge into progress. The same can be said about endeavors to implement principles of diversity, equity, and inclusion (DEI) within the scholarly publishing industry. In her keynote address at the CSE 2023 Annual Meeting in Toronto, Dr Alpha Abebe accentuated the importance of weathering and even embracing the inherent challenges that come with efforts to bring about systemic and sustainable change. And—not unlike the scientific enterprise—one of those challenges is asking ourselves: Are we asking the right questions in the first place? A community practitioner and community engagement researcher, Abebe began by noting her appreciation of the theme of the CSE meeting, “Reflecting on Community: Opening Borders in Scholarly Publishing,” and went on to pose a series of questions that laid bare both the opportunities and the problems that accompany efforts to dismantle barriers within the scholarly publishing industry. Citing a formative experience during her postgraduate studies that shifted her perception of the concepts of data and knowledge, she posited that alternative voices, nonscholarly material, and lived experience are in fact forms of information that can make science […]
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".