Bibliographic record
Abstract
The theory and practice of competency approach has remained significant even decades after its conception. However despite its omnipresence, its validity has been repeatedly questioned. For it to be a truly useful tool, these criticisms and their roots must be critically analyzed to identify improvement measures. To find the solutions, a proper analysis of the competency subject must be first conducted. This paper aims to revisit the prevailing competency theories and backgrounds, with the intention to identify gaps and propose corrective measures. This paper starts by reviewing the theoretical foundations underpinning the competency approach, its origins, frameworks and key criticisms. Based on the reviews, it was found that the approach suffers two limitations. Firstly, its frameworks tend to be bias towards achieving utilitarian objective whereby definition of competent managers is limited to their contribution to organizational economic performance. Secondly, its research were mainly conducted from the positivistic lenses which over-simply the complex nature of managerial work. Based on these findings, the author then proposes epistemological and ideological turns that researchers should consider in researching the competency subject.
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 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 | 0.052 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".