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How to Stigmatize: Inside the Fight for the Rhetorical History of the Seal Hunt

2020· article· en· W3045514159 on OpenAlexaffabout
Jordyn Hrenyk

Bibliographic record

VenueAcademy of Management Proceedings · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsStigma (botany)ReputationRhetorical questionSocial psychologyExtant taxonSociologyPublic relationsCriminologyPsychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Organizational stigma is a collective, negative, moral evaluation of an organization that results in increased threats to its survival. Within the extant literature, stigma is generally conceptualized as an organizational attribute, or something that an organization inherently is. Though researchers have thoroughly interrogated the processes by which organizations manage stigma and minimize stigma transfer to resource-granting stakeholders, we know very little about how an organization actually becomes stigmatized. What turns a negative event into a debilitating organizational label that becomes difficult to shake? Where does stigma come from and how can we distinguish it from related constructs like illegitimacy and poor reputation? By delving beneath the growing typologies of organizational stigma to examine the processes by which stigma emerges, this work demonstrates that organizational stigma is a social process that lends itself to a categorization rather than just a fixed category of organizations. In other words, stigmatized organizations are stigmatized only for as long as their stigmatizers actively stigmatize them. I examine the process of stigma emergence through the case of the stigmatization of seal hunting in Canada by animal rights social movement organizations (SMOs), beginning in the 1960s.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.046
GPT teacher head0.226
Teacher spread0.180 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2020
Admission routes2
Has abstractyes

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