Territory, Rank and Mental Health: The History of an Idea
Bibliographic record
Abstract
We trace the development of ideas about the relation of mood to social rank and territory. We suggest that elevated mood enabled a person to rise in rank and cope with the increased activities and responsibilities of a leadership role, while depressed mood enabled a person to accept low rank and to forego the rewards associated with high rank. This led to the concept of a trio of agonist/investor strategy sets, each consisting of escalating and de-escalating strategies, one set at each of the three levels of the triune forebrain. Depressed mood can be seen as a de-escalating (appeasement) strategy at the lowest (reptilian) level; this should facilitate de-escalation at the highest (rational) level, but sometimes this rational level de-escalation is blocked (e.g., by stubbornness, courage, pride or ambition) and then clinical depression may ensue. These evolved psychobiological mechanisms survived the partial transition from agonistic to prestige competition. We discuss difficulties which have arisen with our ideas, and their implications for clinical work and research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.063 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".