Community by Necessity: Security, Insecurity, and the Flattening of Class in Fort McMurray
Bibliographic record
Abstract
High wages in the oil sands well exceed the Canadian average, making complex class differences less apparent here than elsewhere. This lends itself to a homogenizing narrative of community despite differences in wages, background, citizenship status, and so on. Wolf’s useful counter-framework outlines specific processes by which workers are situated, and by which they situate themselves, in labour hierarchies within the accumulation process. Drawing on interviews and participant observation involving two groups (university-educated immi- grant professionals and high-school educated mine labourers and tradespeople from Newfoundland), we argue that layers of precariousness in both these groups thrust them into “community by necessity.” The unsettling nature of work in the oil sands emerges as a story within a story. In the larger narrative, where “Fort McMurray is jobs,” community is invoked as a place in which household financial security is possible. Inherent in that security, however, is a story of pervasive insecurity wrought by the possibility of injury on the job, paternalism, or redundancies created through company restructuring and economic crises. “Community” is necessary to keep people in place (literally and metaphorically) and at the same time elides ongoing struggles against dispossession.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.030 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".