Bibliographic record
Abstract
My sincerest appreciation goes out to all the women and men who so generously and patiently shared their personal-and at times painful-recollections.Even though I prodded and directed them with specific questions, they often took opportunities to expand and enliven their answers with the warmth of memories long held but rarely shared.The staff of many Ukrainian seniors' centres, lodges, and nursing homes generously identified and solicited knowledgeable informants for me to work with.Without their help, my informant base would have been considerably reduced and the study's contents severely limited.Many individuals also shared names of relatives, friends, or acquaintances who proved to be invaluable sources of information.To all of you generous people, I am deeply indebted for the trust you showed in me and in this study.Some individuals do, however, stand out as being critical to the direction and success of this project.Julie Hrapko, the former Curator of Botany at the Royal Alberta Museum (Edmonton), was instrumental in encouraging, directing, and
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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) | 0.029 | 0.004 |
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".