Expanding Our Conceptions of Embodied and Affective Information Interactions with Queer Theory
Bibliographic record
Abstract
Abstract Embodiment and affect are understudied in information science work to date. Literature that engages with embodied information interactions typically focuses on physical bodies, while work on affect largely centers people's emotional experiences in formal knowledge institutions like libraries. Room therefore exists to grow in our understanding of embodiment and affect, particularly in terms of theorizing how bodies and feelings factor into a wide range of information experiences from non‐dominant standpoints. This panel centers queer experiences and queer theory in order to expand conceptions of and connections between embodied and affective dimensions of information interactions. Panelists will present a range of research that examines queer people's practices and experiences with information in historical, archival, creative, and health‐related domains. Bodies and emotions are essential components of critical queer theoretical perspectives, meaning that scholarship which centers queerness and its intersections with constructs like race has great potential to expand many branches of information science further beyond their normative bents. In concert, topics discussed should spark conversation among attendees about the theoretical and practical benefits of deeply studying embodiment and affect and further utilizing critical theory in multiple domains within the information science discipline.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".