Predicting career progression amongst high potential employees: Job performance, career clarity, developmental implications
Bibliographic record
Abstract
Through the exploration of the job performance-career potential relationship, this study was designed to build both a theoretical and practical bridge in the area of career development so as to integrate differing perspectives on the various fields of study that have explored the topic. The sample consisted of 127 mid-level managers in a large Canadian-based global corporation who recently participated in a high potential early identification development program. No significant relationship was found between participants' level of current performance and potential for career progression. Participants' career potential ratings were significantly predicted by those variables measuring numerical critical thinking, the personality factors of Career Driver and Independent Thinker, and age. Varying competency and development profiles emerged for participants at differing levels of career potential. Competency areas of differentiation included strategic thinking and leadership aspects of executive functioning (higher career potential) versus tactical and individual contributions to the organization (lower career potential). In terms of development, areas of differentiation included a focus on obtaining on-the-job experience (higher career potential) versus skill acquisition and Career/Life Planning (lower career potential). No significant relationship was found between participants' level of career motivation and commitment and current performance ratings. When other variables were accounted for, participants' level of career motivation and commitment was most explanatory in terms of the variance in career potential ratings. These findings illustrate the central importance of individual factors---particularly the concept termed "Career Clarity"--When it comes to integrating various perspectives on the career development process. Theoretical and practical implications are discussed within the context of an integrative framework using systems theory. Limitations of the research results are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".