Movement among Situations: Where Do We Go from Here?
Bibliographic record
Abstract
Examples Interpersonal life is not limited to responding to situations. Social interactions and relationships also unfold through movement among situations, or situation selection , which involves the choice to enter another situation or to change an existing situation in a significant manner. Choosing to enter another situation, or to change an existing situation, brings the individual, the interaction partner, or the pair (or even an entire group) to situations that are “new” (i.e., different from the previous situation) in terms of outcomes, options, or both. Whether to attend a party, visit parents-in-law, or sit close or not so close to a colleague at a dinner party are examples of situation selections that involve entering (or avoiding) a new situation. Also, individuals may choose to interact in “cooperative situations,” when, for example, they seek out each other's company (e.g., “let's have a beer”), or “competitive situations,” when, for example, they seek out a situation in which to compare their strength or ability (e.g., “let's play a game of chess”). These examples illustrate situation selections in which an individual actively seeks to enter a particular situation. However, situation selections are also revealed in changing a situation, such as, for example, stopping work on a joint project, changing the topic of conversation during dinner, or leaving a party early. The reader will note that the above examples illustrate fairly explicit forms of situation selections.
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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 | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".