Bringing Institutions and Inclusion Together: A Multi-Level Theoretical Integration
Bibliographic record
Abstract
Academic siloes are inhibiting progress on both diversity, equity, and inclusion (DEI) and institutional literatures. DEI theorizing has progressed at certain levels, particularly at the individual level, but lacks depth as it relates to societal inputs, indicating opportunities for institutional frameworks. Likewise, institutional theories have progressed to identify various types of institutional work, conditions that enable institutional change, and actor perceptions of institutions that are clearly affected by diverse actors, but have notably avoided contemporary diversity-related empirical contexts that could address paradoxical gaps in theorizing. The purpose of this panel symposium is to formally begin a conversation to explore how the two literatures can be meaningfully integrated so that each may benefit from the knowledge that has been established in the other and future research agendas can draw on integrated theoretical frameworks. We bring together panelists with different perspectives to discuss the most promising opportunities for theoretical integration at different levels of analysis in both the institutions-to-DEI and the DEI-to-institutions directions.
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 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 | 0.030 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.035 | 0.056 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".