Ethical design: a post-Lacanian ethnography of a serious healthcare game
Bibliographic record
Abstract
Purpose This ethnographic case study explores how designers of a serious healthcare game internalise ethical challenges. Design/methodology/approach The analysis uses Jason Glynos’ post-Lacanian theory of ethics with its underpinning notions of fantasy, identification and detachment to interpret how designers wrestle with infantilisation (characterised here as overidentification with fantasy) insofar as it affects elderly people. Findings The research finds that game design ethically benefits when designers experience fantasy traversal. Originality/value Games as a means of promoting healthcare and lifestyle outcomes raise ethical conundrums. To address these concerns, researchers have generally adopted either a normative or a virtue ethics perspective of game design. In so doing, they have neglected how designers actually experience creating a game and, in particular, make decisions about the infantilisation that the process sometimes entails. The present study seeks to remedy such oversight.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".