Developing an Undergraduate Career Conference: Leveraging Mentorship to Promote Career Discovery
Bibliographic record
Abstract
Are students ready for jobs when it comes time to graduation? This is a common question, and one that is often addressed in the media (e.g., Collie, 2019). Despite psychology being one of the most popular degree plans for undergraduate students (e.g., Higher Education Research Institute, 2008), many students in undergraduate psychology programs fail to see the relevance and value of their degree (Borden and Rajecki, 2000). In this work, we designed, delivered, and assessed a career conference for students in psychology. Intentionally different from a career fair where students seek jobs, this event applied a mentorship-based conference model. In this conference model, in addition to professional development training, industry mentors who work in professional fields related to psychology were invited to provide personal insight on their careers in a small-group format. Critical to this model, students were encouraged and able to ask questions that may not be appropriate for a job fair where hiring is happening. Further, this career model involved intentional connections with our Career Services office, allowing for programmatic delivery of career-based content within the domain-specific event. We provide early empirical evidence that this method of career development supports students in learning about career paths that psychology can lead to, identifying skills that will assist them in finding a career, feeling confident in their ability to network effectively, and feeling more connected with professionals in careers related to psychology. We suggest that this model may be beneficial across disciplines.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".