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Record W2736073626

Beyond the Particular and Universal : Dependence, Independence, and Interdependence of Context, Justice, and Ethics

2016· preprint· en· W2736073626 on OpenAlexaff
Marion Fortin, Thierry Nadisic, Chris Bell, Jonathan R. Crawshaw, Russell Cropanzano

Bibliographic record

VenueAston Publications Explorer (Aston University) · 2016
Typepreprint
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsYork University
Fundersnot available
KeywordsNormativeAffect (linguistics)Context (archaeology)CognitionEconomic JusticeEpistemologySocial psychologyPsychologySociologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.095
GPT teacher head0.280
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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