Expanding Historical Approaches to Speculative Design
Bibliographic record
Abstract
This workshop expands historical approaches in HCI and design research, with a particular focus on speculative design.We aim to bring together researchers with diverse orientations and practices to open discussions about historicism and how historical approaches and methods can be applied to critical and speculative design, fostering reflexivity in design practice.Through hands-on activities, we will explore various approaches drawn from the humanities, social sciences, and design studies.The workshop activity topic will revolve around three themes that we believe can be productive for historicizing speculative design: rupture and continuity, figure-ground reversal, and the present as past or future.To that end, we will synthesize insights to develop a research agenda that identifies key topics and possible directions for expanding historical approaches to speculative design and outline a set of design strategies that integrate these theoretical concepts into practice.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".