Prediction and Preparation: Pavlovian Implications of Research Animals Discriminating Among Humans
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
A growing body of evidence suggests that animals of various species can discriminate among the humans with whom they have regular contact. This discriminative ability has considerable implications for research. Because animal life is hedonistic, there is a strong incentive for animal subjects to predict the events that bring them pleasure and pain. Many research settings attempt to deliver hedonic stimuli under strictly regulated conditions without formal warning. Nevertheless, the possibility remains that the presence of a particular human may signal delivery of an important event, thus allowing the animal to prepare for its occurrence. In Pavlovian terms, humans become walking conditioned stimuli, eliciting measurable conditioned responses from animal subjects. These preparatory responses may take behavioral, physiological, and/or motivational forms and modulate the effects of the variables under study. The discussion addresses practical implications of knowing that discrimination among humans by animal subjects may affect one's research agenda.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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