Bibliographic record
Abstract
Mercy- My dear mother Hannah-My wife and friend Jael and Alberta- My two precious and priceless daughters ii ACKNOWLEDGEMENTS Undertaking this exercise has been an enormous task. A lot of sacrifices from different people have made this a reality. Thus, while I bear singular responsibility for the content and errors (if any) in this work, it would be selfish not to recognize the untiring effort that these people have put in. First, to God be the glory! I have always drawn inspiration from the quotation in the bible that I can do all things because of the one who strengthens me. In the course of writing this thesis, any time I have been down, I have drawn strength from Him. Second, my sincere thanks go to my supervisor Professor Marit Tjomsland. Marit, I say thank you for waking me up one day to the enormity of work involved in this study. The hairdryer was really appreciated. This helped me to get rid off complacency. I hope our
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.115 | 0.123 |
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".