Building and Supporting Humanities-Based University–industry Partnerships: View from the Academics
Bibliographic record
Abstract
University–industry partnerships are rare on the humanities side of campus in contrast to the sciences. As a result, little is known about these partnerships, which tend to be with libraries and other not-for-profit organizations. Using the Implementing New Knowledge Environments: Network Open Social Scholarship (INKE:NOSS) as a case study, this research examines a humanities-based university–industry partnership from the academics’ perspective. It explores the nature of the collaboration, associated benefits and challenges, and measures of success and desired outcomes. Overall, building upon several years of working with the partners, the interviewed researchers found that the benefits of collaborating outweighed the challenges. The benefits included the potential to move research towards production-orientated results. Among the many challenges, there was some hesitation about the ability to achieve publications and presentations needed for tenure and promotion. The academics contributed students, and in-kind and cash resources from their own research funds and those of the university to the partnership. At this point, the measures of success and desirable outcomes have not been quantified and instead focus on policy intervention and movement towards open social scholarship. These understandings about the nature of such a university–industry collaboration should provide a good foundation if partnership is funded.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.024 | 0.032 |
| Scholarly communication | 0.037 | 0.022 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".