Bibliographic record
Abstract
STUART PIERSON PROBABLY would have approved of at least half of the title given to the posthumous publication of his reviews and essays on Newfoundland, Hardheaded and Big-hearted: Writing Newfoundland, edited by Stan Dragland (St. John’s: Pennywell Books, 2006). But the “Writing Newfoundland” half would, I suspect, have elicited vintage Pierson scorn. With its once (but no longer) daring suppression of the idiomatic preposition between the participle and the object, this staple of postmodern provocation would have inspired a paragraph, if not a page, of gleeful dismantling (emphatically not deconstructing) on his part. The first half of the title, a phrase of his own creation conjured up for an imagined work, would likely have pleased him. Hard-headed and big-hearted he was, as this collection amply demonstrates, forever questioning the rationality and factuality of what he read or heard said about the place, yet always conveying respect, appreciation, and occasionally affection for the people and the culture. Pierson takes Newfoundland seriously, more seriously at times than some of his subjects might wish. He considers with relentless scrutiny every ponderous pronouncement by experts and every
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".