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Record W3209875393 · doi:10.32920/ryerson.14666280.v1

Clothing, image, and presence: women in the corporate workplace

2021· preprint· en· W3209875393 on OpenAlexaff
Colombe Nadeau-O’Shea

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsClothingPsychologySocial psychologyCognitionSociologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Taken from Introduction: The purpose of this paper is to examine in detail the existing literature addressing the history of Western women in the workplace, the importance of first impressions, gender discrimination and stereotypes in the workplace, and the cognitive and behavioural effects of clothing on both wearers and perceivers. These topics will be analyzed across various fields of study, such as economics, psychology, sociology and gender studies. This review focuses on women as they relate to appearance and professional settings because many of the issues faced by women in professional settings are unique. It would not, therefore, be appropriate to address these issues as they relate to both men and women within the same framework; women’s experiences in regards to these notions deserve special attention. Once these theories are explored and analyzed, they will be expanded upon using ideas of executive presence and how it affects these particular areas of study, as well as how it can be used by women to overcome many of these issues.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.058
GPT teacher head0.252
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes1
Has abstractyes

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