Establishing an online bibliographic database for Canadian Literary Translation Studies
Bibliographic record
Abstract
In recent years Canada has achieved international recognition not only for its prize-winning writers (Margaret Atwood, Michael Ondaatje, Yann Martel, Carol Shields), but also for innovation and leadership in Translation Studies, which has emerged as a relatively new but increasingly vibrant field of scholarly research and publication in our country. In order to facilitate the dissemination and exchange of information about Canadian Literary Translation Studies and foster an increasingly collaborative and international research process, researchers at the Université de Sherbrooke in Sherbrooke, Quebec, Simon Fraser University in Vancouver, British Columbia, and Concordia University in Montreal, Quebec, have established an online bibliographic database of theoretical and critical writing on literary translation in Canada as part of the larger Bibliography of Comparative Studies in Canadian, Québec and Foreign Literatures/Bibliographie d’études comparées des littératures canadienne, québécoise et étrangères . This paper outlines the background of this web-based project and the procedures set in place, as well as the inevitable challenges that may well resonate with other translation bibliographies.
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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.157 | 0.267 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 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".