<i>Talking on the Page: Editing Aboriginal Oral Texts. Papers Given at the Thirty-Second Annual Conference on Editorial Problems, University of Toronto, 24-26 November, 1996</i>, Laura J. Murray and Keren Rice, eds.
Bibliographic record
Abstract
Indexes included are a 'Name Index,' a 'Genre, Language, and Subject Index,' and a 'Printer Index.'In reviewing works of this magnitude one must attempt to be both 'descriptive,' to identify their working parts, so to speak, and at the same time to touch on their potential as basic sources for research.One has been tempted to indulge in some speculations regarding the latter.Suffice it is to endorse what the editors/compilers point out about the wealth of material the combined work discloses of our 'print culture' or 'histoire du livre' and to underline its overall potential as a major source for understanding and appreciating Canadian culture, the way of life of our society-our 'activities and our preoccupations' to quote Marie Tremaine.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.026 |
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".