Incorporating neuroendocrine methods into intergroup relations research
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
Intergroup researchers have the opportunity to access to a wide variety of methods to help deepen theoretical insights about intergroup relations. In this paper, we focus on neuroendocrine measures, as these physiological measures offer some advantages over traditional measures used in intergroup research, are noninvasive, and are relatively easy to incorporate into existing intergroup paradigms. We begin by discussing the major neuroendocrine systems in the body and their measurable biological products, emphasizing systems that have conceptual relevance to intergroup relations. We then describe how to collect, store, and quantify neuroendocrine measures. Altogether, this paper serves as a primer for intergroup researchers interested in adding neuroendocrine measures to their methodological toolkits to enrich the study of intergroup relations.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it