Bibliographic record
Abstract
I would like to express my gratitude to all those who gave me the possibility to complete this thesis. I am deeply indebted to Professor Mark Harman whose valuable advice, hints and warm inspiration guided me in all the time of this project. His unique way of supervision maximized the learning outcome of this project. I am bound to thank two of Mark’s PhD students Yuanyuan Zhang and Shin Yoo who gave me the initial acknowledge of the project and provided constant support. I have furthermore to thank Research Assistant Afshin Mansouri and Zheng Li, PhD students Tao Jiang, for their help by offering me lots of suggestions for improvement. I would like to thank Steffen Christensen from Science and Technology Foresight Directorate of Canada, who gave me useful suggestions on choosing the methods for statistical analysis. Thanks to my landlord Jonathan Rake, my friends Chen Wang and the other Msc Project Students for all their help, support and interest in all the time of this project. I would also like to give my special thanks to my parents whose ongoing love and support enabled me to complete this work.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.664 | 0.570 |
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".