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Record W3005997364 · doi:10.1177/0887403420903370

The Issue of Trust in Shaping the Job Involvement, Job Satisfaction, and Organizational Commitment of Southern Correctional Staff

2020· article· en· W3005997364 on OpenAlexaff
Eric G. Lambert, Linda D. Keena, Stacy H. Haynes, Rosemary Ricciardelli, David C. May, Matthew C. Leone

Bibliographic record

VenueCriminal Justice Policy Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsJob satisfactionOrganizational commitmentSupervisorPsychologyAffective events theoryJob attitudeJob performanceSocial psychologyPublic relationsPerceptionManagementPolitical science

Abstract

fetched live from OpenAlex

While the issue of trust is theoretically essential for the effective operation of correctional organizations, few researchers have examined how the different types of trust are related to salient outcomes for staff. In this study, we examined the effects of coworker, supervisor, and management trust on the job involvement, job satisfaction, and organizational commitment of 322 Southern U.S. correctional staff. The types of workplace trust, however, varied in their effects. Specifically, multivariate analysis indicated only management trust had a significant positive effect on job involvement, but both coworker trust and management trust had significant positive effects on job satisfaction, whereas both supervisor trust and management trust had significant positive effects on organizational commitment. The current findings support the overall contention that workplace trust plays an important role in shaping prison staff job involvement, job satisfaction, and organizational commitment. The results underscore the need for improving perceptions of trust in the workplace, particularly management trust.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.071
GPT teacher head0.355
Teacher spread0.284 · 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 designObservational
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

Citations32
Published2020
Admission routes1
Has abstractyes

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