Bibliographic record
Abstract
This research explored the impact of race and ethnicity on Black, Indigenous, and People Of Color (BIPOC) and their participation throughout the production of evidence synthesis in information science research. The study also analyzed the potential for evidence synthesis team reviewers to face pressure to modify their results based on their experiences and standing in their profession. A team of health sciences librarians and a full-time faculty member serving as director of a health sciences program at a university in the United States created a survey to better understand the possible effect of race and ethnicity on participation in evidence synthesis. The survey was sent to various online listservs and had quantitative and open-ended questions. There were 118 participants (n = 89 for white participants; n = 29 for BIPOC participants). There were significant associations between length in the profession and repercussions, repercussions and article evaluation career, not complementary and repercussions, not complementary and article evaluation and career, change score and article evaluation career, change score and repercussions, and change score and not complementary.
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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 | 0.544 | 0.845 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.052 | 0.031 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".