Self-Regulation in the Career Goal Pursuit Process:Some Novel Perspectives on Career Self-Management
Bibliographic record
Abstract
Due to less predictable career environments, increased career uncertainty, and complex and ongoing changes in jobs and organizational structures, it has become increasingly important for individuals to self-manage their career in order to stay employable, safeguard well-being, and obtain career sustainability. Setting and pursuing career goals is considered to be an important and integral part of career self-management. However, an in-depth understanding of what motivates people to set career goals and how these goals are pursued and adjusted in the face of challenges is largely lacking to date. Therefore, the aim of this symposium is to further our understanding of career goal pursuit by zooming in on various self- regulatory aspects in the career goal pursuit process. Assessing the Career Motivation Profile of Graduate Business Students Author: Gerard Beenen; California State University-Fullerton Author: Serge Da Motta Veiga; NEOMA Business School Work Precarity and Career Goal Progress in Young People Author: Michelle Hood; Griffith University Author: Peter A Creed; Griffith University The Dual Motivational Effects of Perceived Career Goal Progress on Career Goal Pursuit Author: Marijke Verbruggen; KU Leuven Author: Xinhui Jing; KU Leuven Career Adapting Process through the Lens of Goal Adjustment Author: Sarah Darlene Mullen; University of Bern Author: Andreas Hirschi; University of Bern Promoting High-Quality Job Searches through Metacognitive Self-Regulation Author: Justin Weinhardt; University of Calgary Author: Ivy Mai; University of Calgary Author: Janice Ton; University of Calgary Author: Shuhua Sun; Tulane University
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".