Challenging Institutional Racism in International Relations and Our Profession: Reflections, Experiences, and Strategies
Bibliographic record
Abstract
Attempts to create a more inclusive discipline and profession have been commended by many and derided by some. While these attempts have pushed for change, particularly with regards to more equal representation of gender and race among faculty, policies aimed at creating a more inclusive environment are often tokenistic, administrative and bureaucratic, and fail to address structural and institutional practices and norms. Moreover, the administrative and bureaucratic policies put into place are generally targeted at a single categorical group, failing to take into account the manner in which identities are intersecting and overlapping. Equality, Diversity and Inclusion often gets driven by Human Resources and Marketing rather than owned by the wider university. This forum draws from a variety of contributions that focus on describing the lived realities of institutional racism, its intersections with other forms of discrimination, and strategies for change. In putting together this forum, we do not aim to create a checklist of practical steps. Instead, we hope to signpost and make visible the successes and failures of previous challenges and future possibilities that must be taken by both faculty and administrations.
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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.056 | 0.047 |
| Scholarly communication | 0.036 | 0.018 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".