Collaborative research competencies in supply chain management: the role of boundary spanning and reflexivity
Bibliographic record
Abstract
Purpose This paper aims to investigate the competencies that researchers need to develop and employ for successful collaborative research. Design/methodology/approach The authors use a reflexive approach built on participant observation of six cases of collaborative research in public procurement and logistics. Findings The authors identify and explain two major competencies that are required for successful collaborative research. The first is boundary-spanning competence that represents the researchers' ability to move fluidly from the academic milieu to the practitioner's environment. The second is reflexivity competence that allows the researchers to learn from each collaborative research project they participate in and further improve their boundary-spanning competence. Originality/value This study goes beyond the list of skills for collaborative research reported in the literature to describe two major competencies that researchers should develop to perform successful collaborative research. This reflection may serve as a starting point for the development of a sociological understanding of the collaborative research field.
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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 | 0.127 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".