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Record W2125417196 · doi:10.1177/0021886314564013

Contemplating Workplace Change

2014· article· en· W2125417196 on OpenAlexaff
Malvina Klag, Karen J. Jansen, Mary Dean Lee

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsMcGill UniversityMcGill University Health CentreHEC Montréal
Fundersnot available
KeywordsContemplationActive listeningDialogicContext (archaeology)PsychologyAffect (linguistics)Psychological interventionSocial psychologyCognitive psychologyEpistemologyPsychotherapistHistoryCommunication

Abstract

fetched live from OpenAlex

Drawing on topical life histories of physicians in a particularly volatile public health sector environment, we build theory around the contemplation of workplace change. Overall, our study provides evidence as to why single or multiple independent factors, such as pay or job structure, may fail to predict or explain individual decisions to stay in or change workplaces. Instead, the contemplation process we argue is a complex, evolutionary, and context-dependent one that requires individualized interventions. Our findings reveal the prevalence of episodic context-self fit assessments prompted by triggering stimuli, two mechanisms by which thought processes evolved (reinforcement and recalibration), and four characteristic story lines that explain why the thought processes manifested as they did (exploring opportunities, solving problems, reconciling incongruence, and escaping situations). Based on our findings, we encourage practitioners to regularly engage in story-listening and dialogic conversations to better understand, and potentially affect the evolving socially constructed realities of staff members.

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)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.006
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0020.002
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 head0.050
GPT teacher head0.266
Teacher spread0.216 · 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 designTheoretical or conceptual
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

Citations16
Published2014
Admission routes1
Has abstractyes

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