On the Affective Bias of Emotional and Physiological States During Handling Media Content in K-line Mesh-Driven Repositories
Why this work is in the frame
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Bibliographic record
Abstract
The emerging paradigm of ubiquitous computing can be better realized in the presence of intelligent machines that adjust their operation based on the user’s affectively driven behavior or disposition. In this work we provide a method that can be used to investigate how two types of affective attributes influence certain types of affective computing systems. Specifically, our method compares the suitability of emotion-based attributes versus the suitability of physiology-based attributes in affective computing systems that handle media content. We implement our method and use it to evaluate affective computing systems built for a variety of relevant affective attributes. Our findings indicate that, under certain conditions, the emotion-biased systems are more sensitive than the systems which are driven by purely physiology-based attributes.
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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 | 0.000 | 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) | 0.000 | 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 it