Bibliographic record
Abstract
Firstly, I’d like to thank professors Roger Watt and Bill Phillips for their advice and help throughout my PhD. I’d like to thank my partner, friend and willing psychophysical observer Dr Karen Spencer for her support and help throughout the past six years. Harry (the dog) and numerous fish and Canada (the country) for helpful distractions/diversions. My family also deserve my thanks and appreciation for their understanding and support of my continued presence within higher education. I’d especially like to thank my Grandfather, for encouraging an enquiring mind, despite the fact that my enquiries often involved the destruction of his valve-radios. University of Stirling colleagues, Ben Craven, Darragh Smyth and Peter Hancock are owed a great debt of thanks for guiding me when I got lost down dark mathematical alleyways. Also, Tim Chapman for assisting in the reconstruction of my PC after Linux distractions. Emmanuel Stamatakis (Manoli) for being a willing observer and a helpful advisor. My thanks also go to Paul Miller for his helpful advice on undertaking a PhD, which I largely ignored – at my own cost.
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 | 0.001 |
| 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) | 0.006 | 0.016 |
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; both teacher heads 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".