Competition, conflict and cooperation: why and how do they interact socially?
Bibliographic record
Abstract
COMPETITION, CONFLICT AND COOPERATION In the social sciences, historically, economists have emphasized competition over social (strategic) interaction although that has begun to change with game theory, an increasingly important part of microeconomics. Anthropologists, sociologists and political scientists on the other hand have emphasized social cooperation and conflict, traditionally in the form of functionalism and a variety of descendants of Marxism, even though common sense dictates that not all social relationships are cooperative nor are all antagonistic. There, too, game theory is becoming more prominent (Abell 2000). Instead of either the competition or the conflict versus cooperation approach to social relationships and interaction, this chapter adopts the biologists' three-cornered distinction among all three. This helps make clear some general principles under which these alternatives should be expected to be observed as well as their likely consequences. Some examples of the inferences that can be drawn are that things that are often seen as going hand in hand, such as environmental depletion and degradation, can be distinguished as consequences of different courses of action. On the other hand, some things which are traditionally seen as opposed, such as liberal and conservative views of crime as well as conflict and cooperation, may proceed hand in hand. We then go on to explore the use of such concepts in understanding the nature of proto-gender and gender differences and relationships – the most fundamental social relationship that exists among unrelated peers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".