The association between perceived discrimination profiles and career aspirations and expectations of high school students
Bibliographic record
Abstract
Career development literature suggests that perceived discrimination may influence the career choices of individuals from diverse backgrounds (e.g., racial minorities) (Swanson & Fouad, 2020).More specifically, perceptions of both overt and covert discrimination could lead to the elimination of career options (Poon, 2014;Schneider & Dimito, 2010), limiting perceptions regarding career opportunities (Conkel-Ziebell et al., 2019) and attenuated career expectations (Abrahamsen & Drange, 2015).However, perceptions of discrimination are rarely systematically measured in these studies and their association with individuals from diverse backgrounds' career choices seems to be overlooked.Therefore, the study aims to 1) identify profiles of perceptions of discrimination and 2) examine how profile membership relates to key sociodemographic characteristics as well as limiting perceptions regarding career plans, educational aspirations and expectations.Thus, a sample of 756 Canadian high school students (M = 16.3 years old; SD = 0.9) completed an online survey from May 2022 to February 2023.Among them, 52% identified as female, 46% identified as male and 2% identified differently.It is also important to note that the sample is made up of a majority of racialized students (72%) and over a third of immigrant students (38%).Latent profile analyses revealed three distinct perceived discrimination profiles, across which proportions of females, racial minorities and Indigenous people, as well as mean levels of limiting perceptions regarding career plans varied.The conclusion highlights appropriate courses of action to counter the potential adverse effects of perceived discrimination on career aspirations and expectations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".