How People Assess Deservingness and Justice: The Role of Social Norms
Bibliographic record
Abstract
The main function of presenting the evidence for the personal contract theory of justice imperatives and then critically examining the relevant self-interest literature was to set the stage for a theoretical model that attempts to describe how justice and self-interest appear in the normal course of people's lives and influence their reactions to critical events. Before embarking on that it is important to bring some clarity to the essentially important matter of how people assess who deserves what from whom. What rules and standards underlie or are revealed in their judgments of deservingness and justice? Considerable research, as described in the preceding text, supports the hypothesis that people automatically seek out and respond to familiar cues that define who is entitled to what from whom as the primary and initial task in each encounter. The what in this context refers to forms of treatment and ways of interacting as well as to concrete and symbolic outcomes. As discussed in Chapter 5, interpersonal considerations and respect also represent resources that may be allocated (Clayton and Opotow, 2003). Unless and until modified by subsequent events, the automatically generated definition of the encounter provides the basis for what transpires. Having committed themselves to maintaining their personal contracts, people will naturally experience imperatives to comply with the preconsciously and consciously held rules that provide the structure and more or less specific guidelines for their daily activities. But what do we know of these rules?
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".