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We Are “Dogs of the Docks”: Maintaining Occupational Entitativity in the Face of Diminishing Stigma

2023· article· en· W4385210377 on OpenAlexaff
Lucas Dufour, Meena Andiappan

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStigma (botany)Closure (psychology)PsychologySocial psychologyFace (sociological concept)CriminologySociologyPolitical scienceLawSocial sciencePsychiatry

Abstract

fetched live from OpenAlex

This longitudinal study explores how and why a historically stigmatized occupation, longshore workers, choose to maintain the tainted nature of their occupation even as the stigma that once characterized their work largely fades. Relying on data collected from 72 interviews, supplemented with observational and archival data over a 70-year period, we examine how factors both internal (e.g., changes in hiring practices) and external (e.g., technological advances) to the occupation influence longshoremen’s response to diminishing stigma. Significantly, we find that longshoremen work to actively disengage from certain forms of stigma (e.g., theft ), while simultaneously strategically retaining other forms (e.g., violence). Our data suggests that to retain the stigmas that they consider central to the maintenance of their entitativity, longshoremen employ various strategies, including reviving past stigmas and overemphasizing current taint. Answering why an occupation would actively insist on retaining parts of their stigma, we find that longshoremen’s stigma allowed them to enact and justify occupational closure when their jobs – once reviled – become coveted. In contrast to previous work on occupational stigma cooptation, we find stigma being used to repel outsiders, reinforce misconceptions, and maintain negative occupational evaluations. Our findings contribute to the literatures on stigma, entitativity, and occupational closure.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.742
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.077
GPT teacher head0.378
Teacher spread0.302 · 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 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
Published2023
Admission routes1
Has abstractyes

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