Webinar in Partnership with Times Higher Education: <b>Maximizing Research Visibility to Enhance Academic-Industry Collaboration</b>
Bibliographic record
Abstract
Collaboration between academia and industry is essential for driving innovation, economic growth and sustainable solutions to global issues. As industry needs evolve, institutions must ensure that resources across their research ecosystem – from faculty expertise to specialised equipment – are easily discoverable and accessible to potential funders, partners and collaborators. However, identifying partners with the same goals, visions and priorities can be a com-plex process.Advanced research information management systems and centralised profiling tools can help institutions position themselves as valuable partners in innovation by maximising the visibility and utility of research assets and outputs. This webinar, held in partnership between Times Higher Education and Digital Science, will feature a panel of experts who will discuss how these tools help institutions improve the management, accessibility and dissemination of research data to foster mutually beneficial collaborations. It will also include the key findings from Catalyzing Collaboration: How Research Information Management Systems Drive Academic-Industry Partnerships.Topics for discussion will include:The benefits of academic-industry partnerships in research and the challenges in identifying the right partnersFostering strategic partnerships and resource-sharing to train researchers to meet evolving industry standardsStrategies for maximising the visibility and utility of research assets and outputsHow research management tools help streamline management, accessibility and dissemination of research data to facilitate technology transferMeasuring the impact of academic-industry collaborations in researchPanellistsJeff Agnoli, Senior Liaision, Corporate Partnerships, Ohio Innovation ExchangeDerek Newton, Assistant Vice-President, Innovation, Partnerships and Entrepreneurship, University of TorontoChristopher J Rowe, Executive Director for Industry Collaborations, Office of the Vice Provost for Research, Vanderbilt UniversityMike Kagioglou, Pro Vice-chancellor of Research and Business Innovation at De Montfort UniversityKate Byrne, SVP Product, Academic & Publishing, Digital Science
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.368 | 0.161 |
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".