Bibliographic record
Abstract
Mitiarjuk, who has been called the “accidental Inuit novelist” (Martin, 2014), began writingSanaaqin the mid-1950s and was “discovered” in the late 1960s by a doctoral student of Claude Lévi-Strauss. Bernard Saladin d’Anglure took up this text as his anthropology thesis topic, guided its completion, arranged for its 1984 publication in Inuktitut syllabics, and in 2002 published a French translation; his own former student, Peter Frost, has recently (2013) translated the French version into English. Without the training and tools that would equip an outsider to appreciate Inuit writing and the oral traditions from which it arises, and to judge it on its own merits, scholarly assessment by other than specialist anthropologists or ethnographers has often been felt to be beyond the reach of southerners. Nonetheless, a younger generation of literary scholars such as Keavy Martin, inspired by the work of J. Edward Chamberlin, Robert Allen Warrior and Craig Womack, are working to redress such attitudes. Bringing to bear for the first time the perspective of translation studies, this paper will suggest some ways we can move from ethnography’s purported aim of a systematic study of people and cultures to a rigorous and ethical study of these translated texts, reading them explicitlyasliterature, as well as (and perhaps more importantly)asliterary translations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".