Empire, settler colonialism and energy futures : from Jules Verne to Waubgeshig Rice
Bibliographic record
Abstract
Shortly before and during the first decades of the First Industrial Revolution in Europe (ca. 1800-1840), France and most of Western Europe were experiencing wood shortages due to massive deforestation for fuelling their nations with wood and charcoal. As such, France turned to its colonial outposts to import North-American timber, in order to compensate for their shortage. Jules Verne’s The Purchase of the North Pole (1889) fictionalises this struggle; industry capitalists start mining for coal in the Arctic, because of predictions that existing reserves will run out in 500 years, and so justify drastically changing its environment. This oppressive imperial and settler-colonial imaginary continues to have reverberations in the present; Waubgeshig Rice’s Moon of the Crusted Snow (2018) imagines a post-oil native Ojibwe community in Northern Ontario who, stripped of their energy and natural resources, struggle to survive the harsh Canadian winter. Like many other native communities in North America, the Ojibwe people were displaced by settler colonialists who claimed the natural resources that their Native lands held. In this paper, I want to demonstrate how this neglected history of settler colonialism and empire in the search for energy resources still informs energy futures today, through a comparative reading of Verne and Rice’s novels, which sketch out very different, yet incredibly entangled future scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".