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Record W1998661298 · doi:10.1177/0021886301373005

When Organizational Voice Systems Fail

2001· article· en· W1998661298 on OpenAlexaff
Karen Harlos

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsMcGill University
Fundersnot available
KeywordsOrganizational justiceInjusticeEmployee voicePerceptionPsychologyInteractional justicePublic relationsSocial psychologyEconomic JusticeBusinessOrganizational commitmentPolitical science

Abstract

fetched live from OpenAlex

Recently, organizations have been introducing greater types and numbers of systems for employees to voice their complaints. Yet academic and popular accounts indicate that some voice systems are causing what they are intended to prevent, exacerbating employees’ perceptions of unfairness and discontent. Analysis of interview data from an inductive study of employees’ experiences of workplace injustice provides strong evidence of the deaf-ear syndrome (organizational failures to respond to employees’ complaints) and frustration effects (the pattern of increased dissatisfaction when people voice). Informal systems, namely, open-door policies, were particularly susceptible to failure. Drawing on organizational justice theory and industrial relations research, these results are explained, and additional avenues for research and theory development are acknowledged. Implications for individuals and organizations also are discussed.

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)0.007
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.005
Scholarly communication0.0090.006
Open science0.0010.008
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0110.003

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 head0.032
GPT teacher head0.316
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

Citations157
Published2001
Admission routes1
Has abstractyes

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