Roundtable: Revisiting <i>Disrupting Queer Inclusion: Homonationalisms and the Politics of Belonging</i>—Ten Years Later
Bibliographic record
Abstract
This roundtable brings together some of the contributors from our book Disrupting Queer Inclusion: Homonationalisms and the Politics of Belonging (UBC Press 2015) to reflect on their chapters and provide some insights regarding their thinking on homonationalism over the past decade, and where their chapters fit into areas of scholarship and activists movements in the present moment. We were unable to get everyone together in one place to discuss our questions as we were still in the thick of COVID and its protocols, and so we began this textual discussion, having circulated questions to each author; the responses are assembled below, edited for continuity and clarity. It is published now, with the continuing onslaught in Gaza, and the genocides in Sudan and the Democratic Republic of Congo. Join us in this discussion.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.015 |
| 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".