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Third-Party Perceptions of Victims in the Workplace: New Complexities and Opportunities

2024· article· en· W4400439553 on OpenAlexaffabout
Samantha Dodson, Rachael Goodwin, Sarah Jensen, Ho Kwan Cheung, Lynn Bowes‐Sperry

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPerceptionThird partyPsychologyPublic relationsBusinessPolitical scienceInternet privacyComputer science

Abstract

fetched live from OpenAlex

Third parties, or people who learn about or observe others’ mistreatment at work without being directly involved (Skarlicki & Kulik, 2005; Treviño, 1992), play a crucial role in victims’ future workplace outcomes following the initial mistreatment (Dodson et al., 2023). However, the intricacies of the relationship between victims and third parties are under-addressed in the current literature, and we believe scholars have merely scratched the surface of understanding how third-party responses affect victims of workplace misconduct or mistreatment. In this symposium, we present four papers that share novel findings related to how third parties respond in the aftermath of workplace mistreatment and highlight opportunities to continue to expand our understanding of complex interpersonal processes that occur in organizations between third parties, victims, perpetrators, and other organizational stakeholders following mistreatment. Third Parties’ Moral and Social Responses to Workplace Sexual Harassment Victims Author: Rachael Goodwin; Syracuse U. Whitman School of Management Author: Samantha Dodson; Haskayne School of Business, U. of Calgary Author: Jesse Graham; U. of Utah, David Eccles School of Business Author: Morteza Dehghani; U. of Southern California Author: Kristina Diekmann; U. of Utah The Dark Side of Forgiveness: Unintended Consequences of Third-Party Forgiveness on Victims Author: Sarah Jensen; U. of Utah, David Eccles School of Business Author: Xiaoyu Yin; OB Author: Kylie Rochford; U. of Utah, David Eccles School of Business Author: Kristina Diekmann; U. of Utah Taking on EDI: The Ethics of Managers as Modern-day Robin Hoods Author: Samantha Dodson; Haskayne School of Business, U. of Calgary Author: Daniel Skarlicki; U. of British Columbia Moral Disengagement as an Explanation for Failure to Hold Hararassers Accountable Author: Ho Kwan Cheung; U. of Calgary Author: Caren Goldberg; U. de Sevilla Author: Lynn Bowes-Sperry; California State U., East Bay

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0120.025
Scholarly communication0.0160.031
Open science0.0020.015
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.177
GPT teacher head0.460
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes2
Has abstractyes

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