Bibliographic record
Abstract
This article elaborates the emerging career human agency theory and its applicability to career psychology practice. Using Bandura’s human agency theory as a foundation, career human agency theory is a meta-theory that integrates key tenets from major theories in vocational and career psychology. It presents an endeavour of theoretical integration to conceive and understand career issues and vocational behaviours. The article provides a brief overview of career human agency theory, indicating its postmodern constructivist and constructionist worldview in conceptualizing life-career phenomena, while integrating life and career experiences into a dynamic and coherent whole. To this end, the four pillar theoretical principles and constructs of career human agency theory are reviewed, namely, career intentionality, career forethought, career self-reactiveness, and career self-reflectiveness. Furthermore, the article considers and explains the usefulness of the four constructs as they are applied to professional helping and self-helping processes that improve and enhance the vocational wellness of individuals, connecting career human agency theory to practice. In doing so, the article concludes with a case study illustration to demonstrate how these career human agency theory constructs and their related tenets and ideas can inform and guide career development practice and career counselling interventions, utilizing and strengthening agentic functioning in individuals’ worklife wellbeing.
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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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".