Bibliographic record
Abstract
For many management researchers, it is important that the knowledge they create is utilised and has some impact on managerial practice. Sustainable competitive advantage depends less on who has the information and increasingly on those able to make the best use of that information. This paper focuses on two key questions: what are the barriers to research utilisation and what are the most effective strategies for facilitating the use of research by managers in the public sector, based on research evidence? The approach entailed extensive searches of on‐line databases in the fields of management, education and medicine, from the UK, USA, Canada, Australia and Europe. Key themes to emerge from this review were the accessibility and relevance of research, trust and credibility; the gap between researchers and users, and organisational factors. Research use can be facilitated through: support and training; collaboration and partnership; dissemination strategies; networks; and strong, visible leadership.
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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 | 0.366 | 0.565 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.011 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.006 | 0.065 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".