Where data goes to DEI/EDI/DIE: Information Infrastructure and the Protracted Collapse of Institutional Courage
Bibliographic record
Abstract
Slides of the presentation Where data goes to DEI/EDI/DIE: Information Infrastructure and the Protracted Collapse of Institutional Courage, held by Deb Verhoeven in the lecture series Digital Humanities in Focus: Methods, Applications, and Perspectives at the University of Rostock on April 7, 2025. Abstract: Recently my Canadian university abandoned its commitment to diversity, equity, and inclusion (DEI - what is known in Australian as EDI). How did we get here? How did I get here? The answer is: slowly but surely. What seems at first glance to be a dramatic capitulation of knowledge institutions—the shocking cancellation of equity-driven research enquiry and university programs committed to social justice, the overnight mass sackings of librarians and archivists and scientists—perhaps the signs were already there. This talk will provide a personalised perspective on digital information infrastructure in what is shaping to be an destructive era of social injustice and institutional collapse. Bio: Prof. Deb Verhoeven is considered one of the leading experts in the field of new network-based methods for analyzing inequalities in the cultural industries. Since 2019, Deb Verhoeven has held the Research Chair for Gender and Cultural Informatics at the University of Alberta, Canada. In her work, Prof. Deb Verhoeven explores an innovative data-driven approach she calls Social Justice Network Analysis (SJNA): "SJNA can identify patterns of unequal relationships in large data sets. This tool enables researchers, activists, and policymakers to analyze equal opportunities." The focus is not only on individuals, but also on connections, communities, networks, and cohorts. Since 2020, Deb Verhoeven has been an advisory board member of the "Knowledge - Culture - Transformation" Department of the Interdisciplinary Faculty at the University of Rostock. From March 15 to May 10, the Australian-born researcher is a Mare Balticum Fellow at the University of Rostock.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".