Thoughts on Affect: Reading Harold Innis, Donald Creighton, and Sylvia Van Kirk
Bibliographic record
Abstract
This thesis focuses on three fur trade history texts: Harold Innis’s The Fur Trade in Canada, Donald Creighton’s The Empire of the St. Lawrence, and Sylvia Van Kirk’s Many Tender Ties. Using the works and concepts of theorists Donna Haraway, Gilles Deleuze and others, it is argued that each text, through its subject matter, reveals an element of more-than-humanness. The condition of more-than-human calls into question the conception of a human as a stable and singular subject, and the pre-eminent position of the lone human agent within the study of history. This post-humanist analysis allows for a re-reading of these fur trade histories by challenging how the intentional human agent in history is perceived and reproduced in text. In order to initiate a re-reading, each text is re-cast into a different genre in order to prompt the reader into thinking about these history texts in radically different terms. By accentuating the literary nature by which these three historical works operate, the inherent aesthetic and ethical weaknesses imbued in history’s representation and production of the subject and agency are revealed. By putting forth the Deleuzian conception of repetitional thinking as an alternative, this thesis hopes to subvert and ease the grasp of representational thinking on the study of 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".