..... • CAT.HA1uNES ON ACKNOWLEDGEMENTS
Bibliographic record
Abstract
When arriving to Canada almost two years ago I had little knowledge of what the future holds for me. The opportunity to become a master student at Brock University in the Faculty of Applied Health Sciences held a great challenge for me. There are many dear people who I would like to thank, that helped me to complete this investigation and this stage of my life. To my supervisors Dr. Brent E. Faught and Dr. Panagiota Klentrou who were willing to accept the risk of taking me as a graduate student although they knew little about me. They both provided the support and resources which enabled me to pursue this research together with a valuable educational direction. Their advice and patience for every question helped me to bond all the information into one piece. I wish to thank them for building my confidence with my English. It took me time to realize that my abilities in this unique language are not as dreadful as I believed. I express my gratitude to Dr. John Cairney and Dr. John Hay for being part of my committee and for taking time out of their hectic schedules to assist me.
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) | 0.011 | 0.002 |
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".