“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
Bibliographic record
Abstract
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.
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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".