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Record W2804296765 · doi:10.1108/edi-12-2016-0112

HPWS and climate for inclusion: a moral legitimacy lens

2018· article· en· W2804296765 on OpenAlexaff
J.A. Harrison, Janet A. Boekhorst, Yin Yu

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLegitimacyInclusion (mineral)Work systemsSocial psychologyPerceptionPsychologyPublic relationsSociologyPolitical scienceWork (physics)

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to apply insights from the moral legitimacy theory to understand how climate for inclusion (CFI) is cultivated at the individual and collective levels, thereby highlighting the influence of employee perceptions of inclusion-oriented high-performance work systems (HPWS) on CFI. Design/methodology/approach A multi-level conceptual framework is introduced to explain how employee perceptions develop about the moral legitimacy of inclusion-oriented HPWS and the subsequent influence on CFI. Findings CFI is theorized to manifest when employees perceive inclusion-oriented HPWS as morally legitimate according to four unit-level features. Employees with a strong moral identity will be particularly attuned to the moral legitimacy of each of the unit-level HPWS features, thereby strengthening the perceived HPWS and CFI relationship at the individual level. The convergence of individual-level perceptions of CFI to the collective level will be strongest when climate variability is low for majority and minority groups. Practical implications Organizations seeking to develop CFI should consider the role of HPWS and the perceived moral legitimacy of such systems. This consideration may involve policy amendments to include a broadened scope of HPWS. Originality/value This paper explores how employee perceptions of the moral legitimacy of HPWS can help or hinder CFI, thereby offering a novel framework for future inclusion and human resource management research.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0350.001
Scholarly communication0.0000.002
Open science0.0010.028
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 head0.182
GPT teacher head0.378
Teacher spread0.196 · 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.

Study designQualitative
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

Citations7
Published2018
Admission routes1
Has abstractyes

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