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Enregistrement W6906552117 · doi:10.17605/osf.io/2zmu4

“Do I deserve to be called an ally”? A latent profile analysis of social justice allyship and imposterism of lay employees in the workplace.led

2021· other· en· W6906552117 sur OpenAlexaboutno aff

Notice bibliographique

RevueOSF Preprints (OSF Preprints) · 2021
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFeelingProsocial behaviorPhenomenonEquity (law)OppressionSocial justiceInclusion (mineral)InjusticeSocial phenomenon

Résumé

récupéré en direct d'OpenAlex

Within the workplace, employees’ support for and active engagement in equity and inclusion efforts are important in order to meet associated organizational goals of diversity, equity and inclusion. Although underrepresented group members often play an important role in anti-bias efforts in organizations, allies can serve as partners to promote equity and inclusion at work. Allyship can be defined as a quality possessed by individuals who support and advocate for underrepresented group members to challenge systems of oppression (Sabat, Martinez, & Wessel, 2013, p. 480; Washington & Evans, 1991). Despite the numerous benefits and positive outcomes associated with allyship (Warren et al., 2021), many would-be allies may feel inadequate or afraid of making a mistake. The imposter phenomenon is defined as a faulty belief system centered on feelings of uncertainty, incapability or inadequacy and incompetence, resulting in a fear of being exposed as a fake or a fraud (Clance, 1985; Clance & Imes, 1978). In allyship, the imposter phenomenon can occur when individuals feel like they should stand up and speak out, but are afraid of feeling like a fraud or getting it wrong. Imposterism is a vicious cycle, producing feelings of anxiety and depressive symptoms that, in turn, reinforce feeling like an imposter. Allyship imposterism may similarly be detrimental to one’s own wellbeing, for example, by crippling employees’ prosocial intentions and prompting people to second-guess whether it is their place to offer support to coworkers who are treated unfairly based on their identities. In North America, the social dynamics around issues of social justice (e.g., whether it is safe to discuss bias in one’s workplace, norms for when allyship is welcome and/or appropriate) vary within and across workplaces and relationship partners. We assume this complexity poses difficulties for developing allyship competencies while intensifying allyship imposterism. In this study we examine the wellbeing and contextual correlates of lay employees’ allyship functioning (competencies and imposterism), as well as examine the demographic predictors of their allyship functioning. Specifically, we examine whether allyship functioning is associated with personal and job-related wellbeing, work environments where it is psychologically safe to discuss bias, and background characteristics including race, gender, age, leadership role, and whether one has a blue collar vs. white collar job. Latent profile analysis (a person-centered approach) is ideal to holistically capture the most common patterns of allyship functioning (competencies and imposterism) that exist within individuals, allowing for the possibility of identifying subgroups with unusual combinations of allyship (e.g., with high levels of both competencies and imposterism). Unlike a variable-centered approach, this analysis facilitates the description of observed patterns of allyship functioning across a range of allyship competence and imposterism variables operating within the individual, enabling us to identify subgroups of lay individuals (i.e., non-experts) who share similar patterns of allyship functioning. An advantage of this approach is that it characterizes employees in organizational settings according to an existing reality (the whole, complex, individual; Roeser et al., 1998) rather than focusing on individual allyship variables or specific multivariate combinations that may be very sparse in the population (Bauer & Shanahan, 2007). Due to limited empirical data and insufficient theory on this topic, there were no a priori predictions for the number of profiles latent in the data as well as the nature of each profile. Yet, knowing which profiles exist is a necessary starting point for hypothesizing predictors of profile membership. Therefore, prior to preregistering the study, LPAs were conducted separately for two large representative samples of data collected in Michigan and Canada to identify allyship profiles latent within the data. Results revealed four profiles characterized by (a) high competencies/high imposterism, (b) medium competencies/medium imposterism, (c) low competencies/low imposterism, and (d) high competencies/low imposterism. Additionally, the results of the LPAs in Michigan and Canada were nearly identical, increasing confidence in the generalizability of these four common patterns of allyship. Now that we have identified those profiles, this preregistration specifies predictions of profile differences in wellbeing and workplace context, and explores profile differences in demographic characteristics.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,026

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0020,001
Communication savante0,0040,002
Science ouverte0,0010,003
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,031
Tête enseignante GPT0,309
Écart entre enseignants0,278 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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