Keynote: When Good Intentions Just Aren’t Enough: Engaging Diverse Communities as Partners in Knowledge
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
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 […]
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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