Capturing Creativity, 2023 - Presentation 3 - Practice Research Voices: Findings, Recommendations, and Next Steps (Jenny Evans)
Bibliographic record
Abstract
Presentation 3 from the 'Capturing Creativity, 2023' conference on Monday 18th September 2023, given by Jenny Evans, University of Westminster.Jenny’s current role includes responsibility for scholarly communications, research integrity and ethics strategy and policy, research impact, researcher development, research information management systems, and leading a team of subject matter experts. She was the PI on the PR Voices project which built on previous work to build an institutional repository for all research, and which embedded practice research from the beginning, and initial findings in relation to what might need to change in the open standards landscape to better reflect these outputs. Her aspiration is an equitable research (not just scientific) scholarly communications and open standards landscape, which recognises and embeds ‘non-traditional’ outputs and contributors, and which respects and values transparent access espoused by the FAIR Principles, acknowledging that text-based open access models are not always appropriate for these outputs. She started her career as a public librarian in Perth, Western Australia, and after a year of travelling around Canada moved to London where she worked as a science liaison librarian at Imperial College London before moving to a scholarly communications role at Middlesex University. She joined the University of Westminster in November 2016.This presentation highlights the key recommendations and findings of this work (link to published report here: https://doi.org/10.34737/w3803), an update on ongoing work, and give this community the opportunity to feedback on the recommendations.This item contains: MP4 recording of the presentation, powerpoint slides, and audio transcription.
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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 | 0.085 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.040 | 0.031 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.065 | 0.037 |
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".