Leveraging intra-provincial regulatory differences in a post-Paris context: Cryptocurrency mining “reverse battery” strategy in Atlantic Canada
Bibliographic record
Abstract
Cheap and abundant energy is an important incentive for the proliferation of cryptocurrency mining farms. With China's crackdown on bitcoin mining, investors have moved to the United States, Scandinavian countries, and Canada. From the perspective of business, these jurisdictions provide cheap, reliable electricity within a stable institutional context. At the same time, cryptocurrency mining has the potential to generate instability, not only through material demands on the capacity of the electricity grid, but also in jeopardizing governments’ climate goals. This article examines some strategies used by the industry to seek out favorable regulatory environments and take advantage of energy sources and infrastructures, through the case of HIVE blockchain technology, a mining company in Atlantic Canada. The article explains how in contrast with negative reports of marginal employment opportunities and drains on domestic energy supplies, bitcoin miners are developing new narratives to make cryptocurrency mining investment attractive to governments and the public. We find that HIVE has leveraged intra-provincial regulatory differences to expand operations and is currently using a “reverse battery” narrative to improve regulatory and public acceptance of cryptocurrency mining.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".