How well can you tell? Success of human categorisation of horse behavioural responses depicted in media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
) thereby promoting positive human-horse interactions. This study investigated human ability to categorise human-horse interactions depicted in media relative to equine behaviour experts and compared participant scores to their level of interoception. Using an online survey, participants (n = 534) categorised 31 photographs and videos as (overt) positive, likely (subtle) positive, neutral, likely (subtle) negative or (overt) negative human-horse interactions from the horse's point of view and completed the Multidimensional Assessment of Interoceptive Awareness questionnaire (MAIA-2) to assess their level of interoception. Demographic information was also collected (age, gender, education, level of experience with horses, location). Participants differed from expert categorisations of horse affective states across all categories, exactly matching experts only 52.5% of the time and approximately matching experts for positive and negative valence 78.5% of the time. The MAIA-2 did not predict participant ability to accurately categorise human-horse interactions. Women outperformed men in categorising overt positive, overt negative and subtle negative human-horse interactions. Increased levels of education and greater experience with horses were associated with improved categorisation of certain human-horse interactions. More training or awareness is needed to recognise behavioural indicators of horse affect to guide appropriate human-horse activities that impact horse welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it