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Record W4414846554 · doi:10.1108/pijpsm-05-2025-0083

Echoes of silence: how police officers make sense of sexual harassment and the implications for individuals and organizations

2025· article· en· W4414846554 on OpenAlexafffund
Angela Workman-Stark, Prateeksha Pathak

Bibliographic record

VenuePolicing An International Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsAthabasca University
FundersAthabasca University
KeywordsSensemakingHarassmentMisconductPerspective (graphical)Agency (philosophy)Sexual misconductValue (mathematics)Sense of agencyQualitative research

Abstract

fetched live from OpenAlex

Purpose The study aims to investigate how police officers make sense of sexual harassment (SH) and the potential implications for individual officers and their organization. Design/methodology/approach We used a sensemaking framework to analyze qualitative survey data collected at two different time intervals from members of a large North American law-enforcement agency that was grappling with complaints of SH. Findings Our findings illustrate the differing interpretations of SH, including why it persists. Whereas most officers perceived that SH was a systemic issue, some blamed victims or their harassers for perpetuating the issue. Further, reform efforts aligned with the “rotten apple” perspective were viewed as failing to address underlying issues. Originality/value Responses to internal reports of SH by policing organizations are often directed at reforming individuals as opposed to addressing systemic organizational factors. Part of the issue may be the reliance of police leaders on the “rotten apple” theory of police misconduct and assumptions about SH rather than the perspectives of organizational members. Our study illustrates the value of a sensemaking framework for helping create a shared understanding of the current state and developing more effective solutions to address SH. We discuss the implications of these findings for both research and practice.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.389
Teacher spread0.362 · 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
Published2025
Admission routes2
Has abstractyes

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