Where Lie the Similarities and Differences?: A Comparison of University and Industry Partners in Collaboration
Bibliographic record
Abstract
University–industry partnerships are common on the science side of campus where ways to work together are well understood. This is less so in the humanities even as these types of collaborations are being funded by granting agencies and governments. For these partnerships to build a foundation for success, common understandings around issues of the nature of collaboration, benefits, challenges, measures of success and outcomes need to exist. Using Implementing New Knowledge Environments (INKE) as a study case, this research examines a humanities-based partnership to understand similarities and differences in partners’ perspectives around these factors. Overall, the university and industry partners have common understandings of the nature of collaboration, the potential challenges facing the collaboration, and desired outcomes and success factors. However, there are some differences that must be navigated to ensure collaboration success. These focus on the benefits, the role of industry partners, need for tenure and promotion for researchers, and the type of resources that each can provide. While the partnership is in early stages of research, it has had the opportunity to learn about each other and differing perspectives by working and meeting together for over five years. This is the first step to creating a foundation of trust upon which a successful collaboration can be built.
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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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".