Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This monograph examines how HCI conceptualizes, situates, and responds to uncertainty—particularly arguing that our ability to respond to such uncertainties is governed to a great extent by the concepts we use to enframe a single, encompassing, overburdened and slippery idea. We propose four distinct “modes of uncertainty” as a means to begin to draw together the varied strands of work in HCI that address uncertainty in its many forms. The first, and most common, mode is to treat uncertainty as something in need of taming or disciplining. The second mode is to treat uncertainty as generative, or as a resource that can assist in human practices. The third is to look to the politics that shape how we encounter uncertainties and the fourth mode attends to the lived experience of uncertainty through affective dimension.Rather than focus on uncertainty as a discrete phenomenon in the world to be studied, we look to how research goals, methods, and theoretical frames used in HCI research influence the various ways in which we encounter it. By switching from uncertainty (noun) to modes of engaging uncertainty (verb), we foreground uncertainty as a relational concept. We show that it is an active and ongoing condition that designers and researchers make present in different fashions depending upon their priorities and the context in which they are working. We will show that adding modes of uncertainty to our conceptual toolbox facilitates conversation between domains as diverse as disaster risk, maternal health, cybersecurity, and community organizing and lets us draw new connections between disparate areas of research including visualization studies, critical design, feminist epistemologies, and sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it