Finding Fairfield: The Behind the Scenes Story of "Ain't No Harm to Kill the Devil"
Bibliographic record
Abstract
Finding Fairfield is the "behind-the-scenes" story of the writing of Jeffrey Copeland's Ain't No Harm to Kill the Devil: The Life and Legend of John Fairfield, Abolitionist for Hire. John Fairfield was one of the most gifted and notorious abolitionists fighting for freedom for all in the decade before the American Civil War. In the pages of Finding Fairfield, Jeffrey recounts his adventures in gathering the details and information needed to write Fairfield's tale. These adventures took him to historic homes, important landmarks of the pre- Civil War era, Underground Railroad depots/museums, and other sites frequented by John Fairfield and others who proudly carried the torch of abolitionism. Jeffrey's journey was not always an easy one: getting terribly lost in the middle of nowhere while searching the Sandy & Beaver Canal system (a waterway once used to transport runaway slaves, by boat, to freedom), participating in a "ghost tour" near one of the most important Underground Railroad havens, and even spending the night in a haunted inn where John Fairfield himself once slept. Finding Fairfield also recounts Copeland's efforts to re-trace the journey made by John Fairfield when he once led nine slaves from Kentucky to their freedom in Canada. Finding Fairfield is both the story of a writer's craft and an engaging travelogue—a combination sure to please those who love American history and stories of "important Americans" who have had such profound impact on the world we live in today. - Provided by publisher
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 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; 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".