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Record W4390095206 · doi:10.1002/9781118469392.ch10

Respectful Workplaces

2014· other· en· W4390095206 on OpenAlexaff
Michael P. Leiter, Ashlyn Patterson

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of GuelphAcadia University
Fundersnot available
KeywordsPsychologyConstructiveBurnoutSocial psychologyIdentity (music)Work (physics)Balance (ability)Quality (philosophy)Power (physics)ProductivityPublic relationsApplied psychologyPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

This chapter presents a framework for describing how the quality of the interactions influences employees' experience of themselves, their colleagues, and their organization. It considers both the implications for their emotional well-being and for their productivity and their personal identity. Research on workplace social interactions conveys that people have a capacity to perceive social cues and to interpret their implications for one's social standing at work. The job demands–resources model of burnout and engagement assigns definitive power to the balance of demands with relevant resources. The chapter encompasses both the constructive contribution of respectful encounters and the distressing experience of mistreatment at work. Many organizations rely on teams to work together effectively; workplace mistreatment may lead to declines in both the effectiveness and efficiency of teams. The chapter concludes with the implications for enhancing the psychological health of the workplace.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.012
Scholarly communication0.0070.004
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.009
GPT teacher head0.220
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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