Roads to Resources: Uneven Development, Mobile Work, and the Extractive Imaginary in Canada
Bibliographic record
Abstract
In recent years it has become common for workers to commute from high unemployment areas of eastern Canada to work in the Alberta oil sands and related industries. Through a genealogical investigation that moves from the post-war decades to the present, this dissertation asks how, and to what end, mobile extractive work has become normal for east coast communities. Drawing on participant observation and action research, in-depth interviews, and archival research in provincial and federal state archives, I illustrate that welfare state policy, rural restructuring, and targeted recruitment have driven workers into long-distance work, but that a narrow set of stories about work and the economy have normalized these extreme labour relations. In the decades after World War Two, as the Canadian state set its sights on modernization and national economic expansion, it formulated the Maritime region as a national problem. Framing the region’s seasonal economies, low productivity, and “underdevelopment” as barriers to national growth, planners and policy-makers worked to remake Maritime economies and people to fit with a vision of the national economy rooted in industrialization, urbanization, and expanded industrial resource extraction. Welfare state programs focused on rural development, modernization, and labour re-organized local material life in powerful ways, but they also broadcast narrow ideas about what types of work, livelihoods, and economies could be part of the future. These enduring stories about economic viability, I argue, have naturalized decline, mobility, and market volatility, rendering the region’s remarkable reliance on long-distance resource employment ordinary. By obscuring workers’ experiences of volatility and the underdevelopment that shapes extractive labour markets, this normalization has been a linchpin in Canadian extraction. This research highlights the central role of welfare state policy in producing relations and geographies of extraction in Canada. But the state’s intensive investment in managing peripheral workers and regions also underscores the persistence of economies, lives, and forms of valuation that break from the logic of unending accumulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.052 | 0.025 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".