Bibliographic record
Abstract
This entry explores the notion of queer methods and methodologies and how they have been taken up in both the humanities and the social sciences. It begins with a brief overview of what we might mean by the notion of “queer,” its theoretical and conceptual uses, and how researchers have deployed the notion of queer in thinking about ontology, epistemology, and the queer of research methods and methodologies. It then considers the specificities of queer engagements in the humanities and the social sciences as well as various critiques of queer approaches that themselves lead to various erasures and exclusions. The discussion concludes with a focus on how queer methods and methodologies have been used to address limitations and possibilities in how we design and undertake research and the ways in which the knowledges created can address invisibilities, inequalities, and exclusions.
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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 | 0.108 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".