Missing and declining affordances: are these appropriate concepts?
Bibliographic record
Abstract
The concept of affordance has been brought to HCI by Don Norman, who has recently protested against its misuse by designers. They say they will put affordances in the interface, or afford this or that to the users, but Norman points out that affordances only exist inasmuch as they are perceived by users. Therefore, it doesn’t make sense to use the term as designers do. This paper takes the designers’ phrases as a spontaneous expression of design intent and explores the correspondences between these and two of the phenomena captured by communicability evaluation: missing and declining affordances. It highlights some useful distinctions between levels of affordances, and hints at possible links between communicative and cognitive perspectives. It suggests that framing affordances within a broader communicative dimension, and taking advantage of the rhetoric that people use to describe what they are doing, can bring interesting insights to design.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.055 |
| Scholarly communication | 0.008 | 0.057 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".