The Politics of Imaginaries: Probing Humanistic Inquiry in HCI
Bibliographic record
Abstract
Over the past few decades, human-computer interaction (HCI) and interaction design (IxD) scholars have embraced humanistic traditions to cultivate new modes of inquiry: widening examinations of technology’s social constitution, from affective computing [10], aesthetic interaction [4], and experience design [23] to critical race theory [28], post-colonial computing [19], and “the more-than-human turn” [27]. Today, with mounting political and environmental crises, scholars increasingly turn to humanistic inquiry to emphasize the necessity of both critical and imaginative encounters. This work often involves recognizing and reworking systemic inequities baked into the practices, policies, and governance structures associated with computing worlds. The goal of this one-day workshop is to bring together scholars, practitioners, and makers working across HCI and the humanities to develop a concern for the politics of imaginaries. We explore technopolitical imaginaries as the creative connections drawn between past, present, and future possibilities that shape computing development. Across discussions and hands-on activities, we seek to lay the foundation for a broader conversation on the stakes of a humanistic imagination and how HCI might learn from its optimisms without shying away from the necessity of its pessimisms.
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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 | 0.060 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.020 | 0.168 |
| Scholarly communication | 0.029 | 0.032 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".