Red Rose Research Academic Meeting, 2025
Bibliographic record
Abstract
The Red Rose Research Academic Meeting took place on the 19th of November, at Victoria Mill, Burnley. The event was sponsored by The University of Lancashire, The Rosemere Cancer Foundation, NIHR RDN, LSC Integrated Care Board and Lancaster University. The event was chaired by Professor Pierre Martin-Hirsch (Director of R&D, Lancashire Teaching Hospitals) and Professor Umesh Chauhan, (Professor of Primary Care, Mackenzie Clinical Research Institute, The University of Lancashire). The event brought together researchers and clinicians to showcase recent research, share new ideas about clinical problems that need tackling and seek collaborative interest for further developing research throughout the region. Topics discussed included Primary Care and Health Priorities in Lancashire and South Cumbria, Research Delivery, Developing Academic Clinical Medicine in Lancashire & South Cumbria, Developing Research Infrastructure and Clinical Research. The event was an opportunity to: • Hear about current local research projects, • Get involved in planned research, • Develop research ideas, • Develop collaborative partnerships. The program included • Talks by speakers such as Professor Paul Baker (Deputy Post Graduate Dean, NIHR Regional Lead for Academic Training) and Professor Andy Ustianowski, Director NIHR North West Regional Research Delivery Network. • Round table discussion with Trust CEOs, MDs, ICB members and Senior University Medical School Leaders • Presentations and posters of local studies. • Breakout Sessions.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.604 | 0.565 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".