Bibliographic record
Abstract
Despite being born over one hundred years apart, Francis M. Wafer and I have more in common than first glance reveals.We were both born in Ontario as descendents of United Empire Loyalists.We share an Irish heritage.We both attended Queen's University in Kingston.We both left Canada for professional reasons and went to the Republic.In doing so, we both became indebted to the extraordinary people we left at home and the extraordinary people we encountered in the United States.While Wafer's Civil War journey took him into the Army of the Potomac, mine took me into the master's and subsequently the doctoral program at the University of South Carolina.It was there strangely, and not at our alma mater, that I encountered Francis M. Wafer for the first time.While writing my master's thesis on Canadians in the Civil War, I came across a citation to a manuscript collection held by the Queen's University Archives in Kingston, Ontario.Through the generous support of my Gran, Florence M. Ryan, I was able to acquire a complete set of Francis M. Wafer'
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.277 | 0.169 |
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".