Minecraft's territory: Alberta's oil sands, settler knowledge infrastructure and digital geographies
Bibliographic record
Abstract
Abstract In 2017, the Alberta Geological Survey published an extension to the game Minecraft that allows players to virtually mine bitumen in Peace River, one of the three bitumen deposits in Alberta that together form the fourth largest oil reserve on Earth. This article uses the Minecraft extension to advance a novel synthesis of environmental and digital geographies, and to understand how they combine in settler knowledge infrastructures—the networks, institutions and practices through which geoscientific knowledge is constitutive for claims to territory by settler states. To advance these ideas, I show how the data used to create the virtual world within Minecraft are connected to real‐world extraction, especially environmental harms that Alberta's provincial regulator sought to address in Peace River. That data, however, does not stand alone. It was interpreted through, and itself extended, knowledge practices that stretch back to early‐twentieth century mapping and the on‐going collection of extractive data by the state. The Minecraft model also extends Alberta's settler knowledge infrastructure as part of international collaborations with other geological agencies. Set in this broader context, the article pushes digital geographies to attend to how environments—geologic pasts, extractive presents, virtually played—prove constitutive for state claims to territory.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".