Bibliographic record
Abstract
Purpose The competence framework continues to be plagued by unresolved conceptual ambiguity. This paper aims to consider other perspectives of managerial performance. Design/methodology/approach This is a conceptual article that examines and critiques current perspectives of competence based on an extensive literature review. Findings Sources of conceptual ambiguity are rooted in treating competence as both independent and dependent variables in relation to managerial performance. A managerial learning framework and a career perspective may offer less ambiguous, more promising conceptual frameworks for managerial performance. Research limitations/implications The arguments presented for moving beyond the competence framework need to be developed into propositions that can be tested empirically. Practical implications Management development and education activities may not be achieving their intended outcomes due to the level of conceptual ambiguity found with the competence framework. It may be necessary to reconsider the efficacy of development programs and activities that rely on a competence approach. Originality/value Conceptual ambiguity regarding competence has been previously noted. This paper presents an analysis of the possible sources of ambiguity, and the dilemmas faced when trying to reduce the level of ambiguity. By clarifying these issues, the paper may be of value to researchers in the management development field and to practitioners involved with employee and managerial development.
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 | 0.074 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.023 | 0.039 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".