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Record W4296720486 · doi:10.22230/ijepl.2022v18n2a1249

Similarities and Differences in the Structure and Interpretation of Empowerment and Job Satisfaction between Minority and Majority Faculty Members

2022· article· en· W4296720486 on OpenAlexvenueno aff
Jordan Lasker, Jon McNaughtan

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentDemographicsSociologyConstruct (python library)Meaning (existential)PsychologyHumanitiesSocial psychologyPolitical scienceDemographyPhilosophy

Abstract

fetched live from OpenAlex

Faculty empowerment is a more important topic today than ever before, as faculty roles have become increasingly complex. Moreover, an increase in minority faculty has presented universities with the need to understand the complex interactions between demographics and empowerment to better promote employees’ well-being. Past research has found that racial majority and minority faculty perceive their experiences as faculty differently. In this study, we used an empowerment framework and structural equation modeling to investigate similarities and differences in workplace empowerment for a sample of 720 racial majority and minority faculty members. Empowerment was largely similar for majority and minority faculty members, but the construct of self-determination had different meanings for minority faculty members, and it was more strongly related to trust in their institutions and the personal consequences of their work. Moreover, minority faculty members’ beliefs about their capabilities, the specialness of their work, and their ability to make decisions about their work were more important for efficacy, meaning, and self-determination than they were for majority faculty members. Résumé De nos jours, l’autonomisation du corps professoral est un sujet plus important que jamais, dans un contexte où les rôles qu’il est appelé à jouer deviennent de plus en plus complexes. D’autre part, vu le nombre croissant d’enseignants issus de minorités, l’université a besoin de mieux comprendre les rapports complexes entre démographie et autonomisation afin de mieux assurer le bien-être de ses employés. Certaines études ont déjà observé que les majorités perçoivent leur vécu en milieu universitaire de manière différente que les minorités visibles. Dans l’étude actuelle, nous utilisons un cadre d’autonomisation et la modélisation d’équations structurelles afin d’enquêter sur les ressemblances et différences relatives à l’autonomisation au travail dans un échantillon de 720 universitaires de majorités et de minorités visibles. En gros, le niveau d’autonomisation est semblable pour les universitaires majoritaires et minoritaires, mais l’autodétermination a un sens différent pour les minoritaires, celle-ci étant étroitement liée à la confiance qu’ils ont dans leurs institutions et aux conséquences personnelles de leur travail. En outre, comparée à celle des majoritaires, l’attitude des universitaires minoritaires envers leurs propres capacités, le caractère unique de leur travail et leur liberté de prendre des décisions sur leur travail a un plus grand effet sur leur efficacité, le sens qu’ils prêtent à leur travail et leur autodétermination. Keywords / Mots clés : academia, critical race theory, empowerment, faculty, structural equation modeling / milieu universitaire, théorie critique de la race, autonomisation, corps professoral, modélisation d’équations structurelles

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)0.003
metaresearch head score (Gemma)0.012
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.313
Teacher spread0.254 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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