Encounters with Strangers: Lack of Information about a Partner
Bibliographic record
Abstract
Examples Two strangers on a train or plane become interdependent because they are seated next to each other, share an armrest, are able to interrupt each other's reading or thoughts by talking, the one in the aisle seat is required to get up to enable the person at the window seat to go to the lavatory, and so forth. On the first day of classes, two new college students in a Freshman math class are made interdependent by being assigned to work on a particular problem together. Two research subjects, not known to each other, are scheduled for a Prisoner's Dilemma experiment in which the outcome matrix is fully specified. Or, they may be in an “unstructured” situation, left alone together on the pretext of waiting for separate interview rooms to become available, but covertly observed and recorded. In dozens of studies in child development, an infant, either alone or accompanied by its mother, is confronted with a stranger who moves into various degrees of proximity to the child or says or does various things to it. Conceptual Description We describe this as “ encounters with strangers” in order to refer to both the “situation” and the “persons.” This entry is appropriate for our Atlas of situations because, as explained below, each person's lack of information about the unknown partner almost inevitably results in some lack of information about the situation. Thus, the situations for these encounters are located in the “incomplete information” portion of the domain of interdependence situations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".