Conflict of Interest or Community of Collaboration?
Bibliographic record
Abstract
In this chapter we aim to consider both dialectical and dialogical systems, local and regional policies and practice implications for the communication and management of the creative as well as destructive conflict within networks and what else may be needed by cooperating parties as a support infrastructure to assist the development and growth of SME innovation networks. We firstly outline key terms, concepts and issues about innovation, collaboration and the goals set for business incubators by the European Union and globally, contrasting these with each other. We provide an overview of the role of key stakeholders, systems and research analyses, discussion and recommendations indicating our own. These recommendations will be informed by some case studies we have been engaged in as well as the wider research literature canon on these topics.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".