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Record W3010561875 · doi:10.1080/09518398.2020.1735556

Microaggressions experienced by LGBTQ academics in Canada: “just not fitting in… it does take a toll”

2020· article· en· W3010561875 on OpenAlexafffundabout
Brenda L. Beagan, Tameera Mohamed, Kim Brooks, Bea Waterfield, Merlinda Weinberg

Bibliographic record

VenueInternational Journal of Qualitative Studies in Education · 2020
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHeterosexismSexual orientationLesbianInvisibilityTransgenderQueerAbleismSocial psychologySexual identityMinority stressPsychologyIslamophobiaDismissalSociologyGender studiesSexual minorityPolitical scienceHuman sexualityPolitics

Abstract

fetched live from OpenAlex

Given contemporary attention to diversity and inclusion on Canadian university campuses, and given human rights protections for sexual orientation and gender identity, it is tempting to believe that marginalization is a thing of the past for lesbian, gay, bisexual, transgender and queer (LGBTQ) academics. Our qualitative study (n = 8), focusing on everyday experiences rather than overt discrimination, documents numerous microaggressions, the often-unintended interactions that convey messages of marginality. With colleagues, students and administrators, participants reported isolation, tokenism, invisibility, hyper-visibility, dismissal, exoticization, and lack of institutional support. Maintaining constant vigilance and caution was taxing. The everyday microaggressions that lead to isolation and a sense of dis-ease in pervasively cisgender-normative and heteronormative institutions are very difficult to challenge, as they are not the kinds of experiences anti-discrimination policies and procedures are designed to address.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.337
GPT teacher head0.593
Teacher spread0.256 · 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 designQualitative
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

Citations45
Published2020
Admission routes3
Has abstractyes

Explore more

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