What Does It Mean to Measure a Smile? Assigning numerical values to emotions
Bibliographic record
Abstract
This article looks at the implications of emotion recognition, zooming in on the specific case of the care robot Pepper introduced at a hospital in Toronto. Here, emotion recognition comes with the promise of equipping robots with a less tangible, more emotive set of skills – from companionship to encouragement. Through close analysis of a variety of materials related to emotion detection software – iMotions – we look into two aspects of the technology. First, we investigate the how of emotion detection: what does it mean to detect emotions in practice? Second, we reflect on the question of whose emotions are measured, and what the use of care robots can say about the norms and values shaping care practices today. We argue that care robots and emotion detection can be understood as part of a fragmentation of care work: a process in which care is increasingly being understood as a series of discrete tasks rather than as holistic practice. Finally, we draw attention to the multitude of actors whose needs are addressed by Pepper, even while it is being imagined as a care provider for patients.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".