Bibliographic record
Abstract
J. Edward Chamberlin’s If This Is Your Land, Where Are Your Stories?: Finding Common Ground explores the connection between indigeneity and the land. Michael Crummey’s novel, Galore , offers a historical fiction about Newfoundland. “Jack Was Every Inch a Sailor” is a traditional song that inspired Crummey’s novel. This essay combines these texts with Terry Goldie’s personal experience, including his book Fear and Temptation: The Image of the Indigene in Canadian, Australian and New Zealand Literatures , to consider the relationship between Newfoundlanders and Newfoundland. Abstract: Le livre de J. Edward Chamberlin intitule If This Is Your Land, Where Are Your Stories: Finding Common Ground examine le lien entre l’indigeneite et la terre. Le roman Galore de Michael Crummey offre une fiction historique de Terre-Neuve. Jack Was Every Inch a Sailor (Jack etait un marin jusqu’au bout des ongles) est une chanson traditionnelle qui a inspire le roman de M. Crummey. Le present article combine ces textes avec l’experience personnelle de Terry Goldie, y compris son livre intitule Fear and Temptation: The Image of the Indigene in Canadian, Australian and New Zealand Literatures pour etudier les liens entre les Terre-Neuviens et Terre-Neuve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".