How Career Influencers Can Promote Sustainable Careers and the Wellbeing of Underrepresented Students
Bibliographic record
Abstract
Most students pursuing postsecondary education have a primary goal of attaining gainful employment and enhancing their career prospects. Yet postsecondary career centers, whose mandate is to serve all students and sometimes alums, are often under-resourced, especially when it comes to providing catered support for underrepresented students. By the same token, students also prefer to turn to career influencers: postsecondary professionals with whom they regularly interact but work outside of career centers and might not have career development expertise. Though they informally support student career development, career influencers are trusted members within students' career ecosystems who contribute to student wellbeing, career, and lifelong success. This chapter examines career realities and challenges experienced by three underrepresented student populations (international students, students with disabilities, and Indigenous students) and offers recommendations on leveraging career influencers to sustain students' career ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".