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Record W2480046083 · doi:10.5539/ijps.v8n3p84

Workplace Romances: “Going to Work Is Amazing and Really Fun”

2016· article· en· W2480046083 on OpenAlexvenueno aff
Lars Göran Wallgren Tengberg, Inga Tidefors

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExcuseRomancePsychologyFeelingSocial psychologyThematic analysisFocus (optics)Point (geometry)Work (physics)WorryQualitative researchPsychoanalysisSociologyLawSocial sciencePolitical science

Abstract

fetched live from OpenAlex

<p>Much of the research on workplace romance has been conducted in the United States, and the focus has often been on negative consequences and risks for gender discrimination. The purpose of this study was to obtain an understanding of workplace romance through the point of view of those involved. Data were collected from five female participants and five male participants in separate focus groups; all participants had started a love affair at work. The thematic analysis of the transcripts showed that having a workplace romance was mostly a positive experience. However, all participants described the need to keep their romances secret for two main reasons: one, to avoid possible negative consequences, and the other, to enjoy feelings associated with a secret love affair. To some degree, the participants seemed to excuse themselves more readily than others whose behavior they thought was worse. When asked about workplace rules and policies, the participants wavered between expressing negative views and describing situations where rules could be needed.</p>

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.221

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 studies0.0000.000
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 head0.125
GPT teacher head0.468
Teacher spread0.344 · 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 designNot applicable
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

Citations5
Published2016
Admission routes1
Has abstractyes

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