Sustained success through the management of core competencies: an empirical analysis
Bibliographic record
Abstract
The authors examine the relationship between the management of core competencies and long-term success for functional quality teams. The analysis focuses on the following hypotheses: functional quality teams in high-technology companies that are organized around the management of core competencies are more successful in the long run than functional quality teams which are not; different functional quality teams have different core competencies: the portfolio of core competencies depends on the specific role played by the team; and the portfolio of core competencies of a functional quality team evolves with the changing role of the team. Even though the amount of data is too small to be able to show statistical significance, they do tend to show support for all three hypotheses. A result of special interest was the suggestion that, even though core competencies management seems to be associated with success, none of the core competencies management steps taken individually will bring success to the functional quality team. The four steps of core competencies management seem to have a synergistic effect that can result in better performance.>
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".