Hybrid Vision Panel: Progress Not Perfection: DEI Work Within Information Organizations
Bibliographic record
Abstract
Diversity, Equity, and Inclusion (DEI) are very important to any organization, especially libraries and information organizations. However, as the initiative has gained prominence, there seems to be a lack of systematic and comprehensive approaches to expanding diversity, equity, and inclusion within organizations. This dearth of strategy often leaves the burden of diversity, equity, and inclusion on a single individual or a few individuals, particularly those who identify as Black, Indigenous, and People of Color (BIPOC). As is common in the information field, responsibilities can seem like a never-ending list. This, coupled with a lack of organizational direction, can make it difficult to achieve progress with this initiative. Presenters Dr. Kawanna Bright, Sarah Dupont, and Maha Kumaran addressed the lack of a cohesive approach and how progress in diversity, equity, and inclusion depends on many factors, including an organization’s leadership, demographics, and geographic location. The presenters discussed the importance of such work and addressed questions from the audience about their personal insights and challenges.
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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 | 0.032 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".