Bibliographic record
Abstract
This book explores speculation in design research in the field of human-computer interaction (HCI). The authors reveal how speculative reasoning in design research increases the capacity of HCI to address a wider array of social and research challenges. Speculation in design research employs (1) leaps of imagination, (2) diverse ways of knowing or epistemologies, (3) ethical reflexivity, (4) and makes alternate possibilities experiential. This book shows how each can be productively and critically applied together through existing, emerging, and new research approaches in HCI. The aim of this book is to generously see speculation as more than a form of critique or genre of design research, to instead be seen as broadly central to the material investigations that govern much of the field. In doing so, the book aims to expand the potential role of speculation in HCI and shows how speculation is applicable to a wide range of research goals, which, in turn, creates research approaches in new directions. In expanding the approach and methodology of speculation in HCI, the books draw inspiration from other disciplines and intersectional perspectives. By examining current, emerging, and possible new forms of speculation methods, this book will be of interest to undergraduate and graduate students in HCI as well as seasoned researchers and practitioners
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".