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Record W3168883303 · doi:10.1177/21582440211023140

Analysis of “Yes” Responses to Uniformed Police Marching in Pride: Perspectives From LGBTQ+ Communities in St. John’s, Newfoundland and Labrador, Canada

2021· article· en· W3168883303 on OpenAlexaffabout
Sulaimon Gıwa, Roddrick A. Colvin, Karun Kishor Karki, Delores V. Mullings, Leslie Bagg

Bibliographic record

VenueSAGE Open · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of the Fraser ValleySt. Thomas UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsPrideTransgenderQueerLesbianGender studiesPopulationSociologyParadeCriminologyPolitical scienceLawDemography

Abstract

fetched live from OpenAlex

Recently, a number of Canadian police forces have been banned from Pride parades. A ban on uniformed police in these parades has proven to be contentious; the general public and lesbian, gay, bisexual, transgender, queer, and plus (LGBTQ+) communities have been split on the issue. Limited research has examined the perspectives of the general population or, until now, LGBTQ+ people on this matter. Using an online survey designed to gather ideas or opinions of LGBTQ+ community members regarding their hopes, aspirations, and vision for the St. John’s Pride board, 181 LGBTQ+ respondents responded to this question: Should the police be allowed to march in uniform at the St. John’s Pride parade? In total, 92 (51%) said “Yes.” A critical analysis of their qualitative responses revealed four interrelated themes: (a) power of Pride, (b) “they are we and we are they,” (c) “the police are on our side,” and (d) taking back Pride. Implications of the findings for police-LGBTQ+ community relations are discussed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.372
Teacher spread0.336 · 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.

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

Citations3
Published2021
Admission routes2
Has abstractyes

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