Evolution of the heuristic inspection: towards an integration of accessibility, usability, emotion and persuasion?
Bibliographic record
Abstract
This research investigates the issue of conducting heuristic inspections along an extended array of dimensions that includes often ignored considerations such as motivational and persuasive factors. Two objectives are pursued: to offer a comprehensive perspective on the different approaches to heuristic inspections and their evolution in the last decades since their emergence, and to propose an approach that integrates the extended array of heuristic criteria. The motivation for the proposed approach and the issues faced are also discussed. Cette recherche porte sur l’inspection heuristique des interfaces selon un éventail de critères qui relèvent de plusieurs dimensions, notamment de ses aspectssouvent ignorés lors de l'inspection comme la motivation et la persuasion technologique. Elle poursuit deux objectifs : d’une part présenter un panorama desapproches en matière de critères d’inspection ergonomique et d’autre part à proposer les bases d'une approche qui intègre un élargissement des critères d'analyse heuristique des interfaces. Les motivations et enjeux de l'approche sont aussi présentés.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".