Bibliographic record
Abstract
Joanna Chataway & Catherine Lichten speak to Ellen Clark, Commissioning Editor: Joanna Chataway is a Director of the Innovation, Health and Science Group at RAND Europe. She has held senior positions and appointments across a range of academic, policy research, consulting and research funding bodies. She has >25 years of experience in the areas of research, innovation and technology policy. She has particular expertise in the fields of global development and health innovation and has researched extensively the range of factors that influence the rate and direction of product and process innovation in health. Her research has spanned public and private sectors and she has worked in industrially developed and developing countries. She has researched and reported on regulation, standards, public opinion, finance, institutional and organizational arrangements, public/private partnerships, intellectual property and clinician and patient behaviors in relation to medical and health innovation. In addition to her RAND appointment, Chataway is a professor at The Open University and is currently a senior leader of the Innogen Institute (formerly the ESRC Innogen Centre), which conducts a broad range of interdisciplinary research projects on social and economic aspects of innovation in life sciences. Innogen is based at The Open University and The University of Edinburgh. Chataway received her PhD from The Open University. She is a member of the Phi Beta Kappa Honors Society. Catherine Lichten is a researcher in the Innovation, Health and Science Group at RAND Europe. Her research interests focus on strategies for supporting research and innovation, particularly in health. Her recent work includes an assessment of the UK’s dementia research landscape and opportunities for capacity-building, and an international mapping of the mental health research funding landscape. Prior to joining RAND, she worked as a journalist covering life sciences research policy in the UK and the Europe. She holds a PhD in computational biology from McGill University in Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".