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Record W2117517883 · doi:10.1177/1049732308322604

Gauging Visibility: How Female Clerical Workers Manage Work-Related Distress

2008· article· en· W2117517883 on OpenAlexaff
Bonita C. Long, Wendy A. Hall, Nicole Bermbach, Sharalyn Jordan, Kathryn Patterson

Bibliographic record

VenueQualitative Health Research · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsVancouver Coastal HealthUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsVisibilityDistressFlourishingVulnerability (computing)Work (physics)Affect (linguistics)Social psychologyPsychologyPublic relationsGrounded theorySociologyQualitative researchPolitical scienceEngineeringComputer securityClinical psychologyComputer scienceGeographyCommunication

Abstract

fetched live from OpenAlex

Our aim was to explain how female clerical workers manage work-related distress, using a feminist grounded theory method. Thirty-seven interviews were conducted with 24 female clerical workers. They engage in the process of gauging visibility to manage a recognition-vulnerability paradox. To gauge visibility, they take the lay of the land by attending to threats, resources, and supports within withering or flourishing work conditions. When distressing events occur, they select tactics of taking it in, taking it on, or letting it go, which are influenced by the quality of their work conditions. Their efforts to manage distress affect their workplace visibility, potentially enhancing their recognition or exacerbating their vulnerability. Gauging visibility can either diminish or enhance employees' health and well-being. Our findings emphasize social processes and structural conditions, shift attention to organization-wide efforts to alter workplace conditions, and suggest initiatives that enhance employees' opportunities for recognition, safety, and collective actions.

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)0.021
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0050.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.591
GPT teacher head0.624
Teacher spread0.032 · 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; both teacher heads agree on what is shown here.

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

Citations13
Published2008
Admission routes1
Has abstractyes

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