Independence: We Go Our Separate Ways
Bibliographic record
Abstract
Examples Situations characterized by mutual independence are those in which neither individual has preferences or aversions regarding the partner's possible behaviors. For example, two students share a large apartment in which each one has a separate room for sleeping and studying. When they retire to their separate rooms, each may behave as he or she wishes without affecting the other's outcomes or being affected by what the other happens to do. Similarly, estranged partners who “go their own way” and are indifferent to one another's actions are mutually independent. Neither person's actions have any impact, for better or for worse, on the well-being of the other. If neither partner in a close relationship derives any benefits or costs from the other's reading habits, then the partners' choices of reading material illustrate mutual independence. Each partner reads what he or she wishes to read, with no implications for the partner's outcomes. (Of course, research on “social facilitation” [Zajonc & Sales, 1966] suggests that exceptions may occur when the partners' mere presence in the same room affects each other's enjoyment and effort. Conceptual Description The requirement for the situation of mutual independence is that each person's outcomes are affected only by that person's actions and not by what the partner does nor by what the two do as a pair. Because each person is affected by his or her actions, we may say that each person's outcomes are determined by “actor control.”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".