How Career Development Professionals Can Close the Gap Between Human Resources and Gen Z
Bibliographic record
Abstract
Generation Z (Gen Z) is about to be the world's largest and most educated cohort of workers. Human resource managers (HRMs) now rely heavily on the human capital of post-secondary education (PSE) Gen Z graduates. Yet, they struggle to understand how Gen Z graduates' work motivations differ from those in previous generations. This chapter proposes that career development professionals (CDPs) working in PSE can help to create sustainable relationships between Gen Z graduates and HRMs. The chapter begins with a review of Gen Z work motivations and HRM's efforts to satisfy those. It then reviews the current model of CDPs' roles (one that focuses on educating students about HRMs) and proposes an extension to that role (one that focuses on educating HRMs about graduates). Practical examples of this reimagined role in action are provided. Ultimately, the chapter offers a new way of thinking about how CDPs facilitate successful school-to-work transitions and contribute to sustainable relationships between Gen Z graduates and HRMs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".