Bibliographic record
Abstract
Purpose This study seeks to examine the extent to which the use of career center services results in the significant incremental prediction of career outcomes beyond its established predictors. Design/methodology/approach The authors survey the clients of a public agency's career center and use hierarchical multiple regressions in order to examine the extent to which it achieved its goals. Findings Career center usage predicted career resilience and action as well as perceived management commitment to employee development beyond established predictors for these variables. Employees' belief that they were personally responsible for their careers was the prime predictor of career center usage. Research limitations/implications The primary limitations of this research are the cross‐sectional research design, the self‐selected sample, and the single source of survey data. Practical implications Making a career center available to employees can help them adjust to the new protean career model and an environment of considerable downsizing. Using the career center leads to positive results with respect to the perceptions of management. Originality/value Although the cultural barrier to career center usage is likely to be resolvable only over time, supervisors could be encouraged to offer more support and coaching to employees. Also, employees' jobs can be developed by increasing the extent to which supervisors provide feedback to employees, allow employees to work autonomously, and require the use of multiple skills.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".