HPWS and climate for inclusion: a moral legitimacy lens
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".