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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".