Third Parties: Effects of an Outsider
Bibliographic record
Abstract
Examples There are many situations in which a third person is involved in a problem with a dyad. For example, when the dyad lacks information necessary for an important decision, a third person may appear who is able to provide it. As an instance of Entry #17 (Joint Decisions under Uncertainty), a couple in a strange city is not sure whether to enter the restaurant immediately before them or to try to find a better one among possibilities further down the street – along which they cannot see very far. A local resident passes by and they ask his opinion. A family therapist helps a feuding couple find mutually satisfying coordination solutions to the problems of meshing their conflicting schedules and increasing their periods of relaxation together for romantic interludes. Two drivers arrive simultaneously at an intersection with four-way stop signs and are uncertain as to who should proceed first. They are aided in this coordination problem by a policeman, who signals for one to wait and the other to go ahead. Two sisters are in strong disagreement about what they should wear to school on the first day. They turn to their older sister and each tries to get her to support their particular preference. Or, they may be quarreling about the use of the bathroom they share. Their mother intervenes, clearly states the value the family places on harmony and fairness, and suggests that they take turns.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 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".