Pierre BonnechereThe Sea as a Two-Way Passage between Life and Death in Greek Mythology
Bibliographic record
Abstract
I wish to thank all those who have helped me achieve my goals in higher education. Dr. Pierre Bonnechere and Dr. Egbert Bakker were my first mentors at the Université de Montréal, Centre d’Études Classiques. Dr. Bakker ignited my curiosity for Greek literature and encouraged my early efforts at research. His open-mindedness was and remains a model in my academic life. Dr. Bakker provided much-needed moral support and advice throughout my undergraduate and graduate studies. I am grateful for his friendly mentorship. Dr. Pierre Bonnechere, with his rigorous teaching and no less rigorous grading, helped me develop serious research methods. His scholarship and lectures inspired me to always keep learning and to pursue every thread of my research thoroughly, no matter how insignificant or fanciful some details may seem (as for instance, dolphins or aquatic birds!). I am thankful for the fascinating discoveries I have made in this way, and I deeply appreciate the generous gift of his time and attention with my research and graduate studies. At the University of Texas at Austin, Dr. Paula Perlman has been my mentor and dissertation advisor. Under her guidance I have discovered the study of epigraphy, and
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".