Articulating resistance : translating Acadie & Ireland as post-colonial and subaltern
Bibliographic record
Abstract
This thesis addresses the comparison of Subaltern Acadie and Ireland and the resistance in translation that they manifest to counter colonialism and Empire. Though separated temporally, Ireland and Acadie share key characteristics: a basis in orality, post-colonial history, and Subaltern status. These qualities provide a common grouond for the comparative analysis of how both societies access discourses of power - an effort manifested through translation. The corpus consists of: Acadie - Henry Wadsworth Longfellow's Évangéline and its French Canadian translation by Pamphile LeMay; Ireland - Tain Bo Cualgne and its English translations by Lady Augusta Gregory ( Cuchulàin of Muirthemne ) and Joseph Dunn ( The Ancient Irish Epic Tale Tàin Bo Cualgne, The Cualgne Cattle Raid ). The Acadien text manifests passive resistance by repeating historical inaccuracies, emphasizing Orientalizing practices and essentialism, and the insertion of LeMay's political ideology into the translated text. The Irish texts manifest active resistance through Irish nationalist-oriented translations that can be twinned with temporality and key political events, and widely variant translations that demonstrate Ireland's long translation history and tradition. In terms of methodology, this thesis draws upon research and theories from: Post-Colonial Studies, Translation Studies, as well as Acadien and Irish political and cultural history.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".