Bibliographic record
Abstract
The term ‘extractivism' has quickly become the name for every process and practice through which value is generated for capitalism. Given this conceptual ubiquity, what analytic function does this term actually serve? This Afterword to a special issue on Extractivism reflects upon possible reasons why scholars in the humanities have recently become interested in resource extraction, among which is the desire for one's scholarly work to respond to a gathering sense of planetary environmental crisis. Such engaged scholarship may, however, be premised on faulty assumptions about the realms of discourse and experience where it can have effects. Extending Stuart Hall's insights about the need for both commitment and circumspection regarding the work our research can do in the world, this essay considers the relationship between resource extraction as a moment and process under capitalism (or socialism), and extractivism as an ideology and cultural logic that permeates social imaginaries as well as literary and other discourse. Keeping an eye on the materiality of relations and processes dubbed “extractive” is one way of avoiding the conceptual creep, metaphorical inflation, synonymical restatement, and loss of analytical precision that is now developing in the use of the term ‘extractivism.’
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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 | 0.039 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.014 | 0.114 |
| Scholarly communication | 0.033 | 0.063 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.012 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".