Bibliographic record
Abstract
Abstract Government-run geological surveys have increasingly facilitated exploration for potential mines by inviting novice prospectors to sift through old datasets prior to visiting physical sites, a process known colloquially as desktop prospecting. In northern British Columbia, Canada, some novices have developed sophisticated techniques for analyzing promising signs in these data and narrativizing their own desktop prospecting labor within broader environmental and economic shifts playing out across rural Canada. This article examines how efforts to vernacularize simulation-based geological expertise into new forms of work-from-home labor is transforming the ways settler entrepreneurs articulate attachments to rural areas. This growing interdependence of entrepreneurial web-based prospecting and extractivism writ large underscores a fundamental transition in how government ministries and developers relate the development of mines to the making of homes. Computer modeling tools have transformed prospectors’ relations with people and places by altering where and how they conduct day-to-day work. The valorization of model-work as an accessible, democratizing practice has also shaped how prospectors discern what kinds of homes bear the risks of mineral exploration labor. With free maps and simple analytical software in hand, BC-based geotechnical institutions insist, individual prospectors might yet play critical roles in luring mineral exploration companies back to the region after a decades-long decline in mining activity. As climate change renders regional timber extraction uncertain and mining industry restructuring continues apace, settler prospectors’ homemaking aspirations are turning inward toward domestic spaces of labor—some of the few spaces where precariously employed resource workers can still maintain illusions of control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".