Beyond the Particular and Universal : Dependence, Independence, and Interdependence of Context, Justice, and Ethics
Bibliographic record
Abstract
This article reflects on context effects in the study of behavioral ethics and organizational justice. After a general overview, we review three key challenges confronting research in these two domains. First, we consider social scientific versus normative approaches to inquiry. The former aims for a scientific description, while the latter aims to provide prescriptive advice for moral conduct. We argue that the social scientific view can be enriched by considering normative paradigms. The next challenge we consider, involves the duality of morally upright versus morally inappropriate behavior. We observe that there is a long tradition of categorizing behavior dichotomously (e.g., good vs. bad) rather than continuously. We conclude by observing that more research is needed to compare the dichotomous versus continuous perspectives. Third, we examine the role of “cold” cognitions and “hot” affect in making judgments of ethicality. Historically speaking, research has empathized cognition, though recent work has begun to add greater balance to affective reactions. We argue that both cognition and affect are important, but more research is needed to determine how they work together. After considering these three challenges, we then turn to our special issue, providing short reviews of each contribution and how they help in better addressing the three challenges we have identified.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".