Risk Mismatches and Inequalities: Oil and Gas and Elite Risk-Classes in the U.S. and Canada
Bibliographic record
Abstract
There has been a recent renewal of approaches to the study of class and inequality, including Bourdieusian class analysis, the new economics of inequality, and Marxist class approaches. Despite the importance of these approaches, they have a common baseline that this paper problematises. This baseline is that these approaches to inequality identify the economic dimension of inequalities as one in which a series of goods are produced, and then different individuals or groups are able to employ certain types of powers to disproportionately appropriate or accumulate these goods. Without denying the importance of inequalities in goods, this paper focuses on another set of processes that are interacting with the process of the distribution of goods — the production and distribution of risks. This paper employs the concept risk-class to analyse how inequalities are emerging from systematic mismatches between a group’s share of the benefits from the production of risk and their share in the damages from the distribution of these risks. Bringing together an analysis of the oil and gas industry with recent discussions of inequalities emerging from financial risk, this paper identifies risk-class-elites whose advantageous risk positions are secured at the cost of intensified risks for the already least advantaged.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".