More than how you start or finish: performance trajectories predict interns’ post-graduation vocational outcomes
Bibliographic record
Abstract
Purpose Internships are a common form of short-term employment for students seeking to demonstrate their value to employers and thereby improve their post-graduation career opportunities. As such, performance during the internship has been found to be positively related to post-graduation vocational outcomes. Yet, internships also serve a developmental purpose, wherein students’ performance is expected to change over time. We extend previous research by taking into account this dynamism and examining the relative validity of interns' job performance trajectories compared to static indicators of interns’ performance for predicting post-graduation vocational outcomes. Design/methodology/approach We analyzed data from 465 engineering interns at a large Canadian university. Interns' performance was evaluated by supervisors at three intervals during their internships. Employment outcomes were assessed through a survey 6–12 months after graduation. Using latent growth modeling, we assessed the predictive validity of performance trajectories above initial, average and final performance evaluations. Findings Performance trajectories positively predicted receiving a job offer from the host organization and post-graduation salaries. This effect was consistently observed in almost every instance, controlling for initial, average, and final performance evaluations. Originality/value This study introduces the concept of performance trajectories as a critical predictor of internship success. Additionally, it contributes to the dynamic job performance literature by presenting evidence for the robustness of evaluators’ preference for performance trajectories, even in a field setting where memory heuristics may dampen the predictive effect of trends. Practically, this study provides guidance for interns and intern-support staff seeking to optimize internship experiences for career benefits.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".