“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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».