Bibliographic record
Abstract
University-industry partnerships are common in the Sciences, but less so in the Humanities. As a result, there is little understanding of how they work in the Humanities. Using the Implementing New Knowledge Environments: Networked Open Social Scholarship (INKE:NOSS) initiative as a case study, this paper contributes to this discussion by examining the nature of the university-industry partnership with libraries and academic-adjacent organizations, and associated benefits, challenges, measures of success, and outcomes. Interviews were conducted with the collaboration’s industry partners. After several years of collaboration on the development of a grant application, industry partners have found the experience of working with academics to be a positive one overall. Industry partners are contributing primarily in-kind resources in the form of staff time, travel to meetings, and reading and commenting on documents. They have also been able to realize benefits while negotiating the challenges. Using qualitative standards, measures of success and desired outcomes are being articulated. This work developing the partnership should stand the larger INKE:NOSS team in good stead if they are successful with securing grant funding.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.001 | 0.027 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".