p176 Maine's CMP Corridor as a "Paracommons": The Spatial Politics of Gains and Harms among Proprietors, Neighbors, Sociological Systems and the Wider Economy
Bibliographic record
Abstract
Schemes for linking hydropower to electricity users in the northeastern US have ignited intense controversies that have slowed down or sunk several infrastructure projects. Maine's Spanish-owned utility monopoly, Central Maine Power, proposed the most recent transmission project, the New England Clean Energy Connect (NECEC), or CMP corridor, a $1 billion 145-mile transmission line to bring electricty from Hydro-Québec to Massachusetts through western Maine. Proponents of the CMP Corrideor highlight the project's carbon impacts, rate savings, construction jobs, mitigation package, and property tax impacts. But, the CMP Corridor has also drawn intense grassroots opposition for its impact on fisheries, wildlife, recreational tourism, scenic amenities, and development of domestic renewables. The Corridor opposition has been dismissively characterized as "not in my backyard" (NIMBY) politics, but multiple sessions of public testimony by opponents and proponents articulated a wide range of perspectives and engaged in wider critiques that can be effectively understood as competing claims on the "paracommons." This article uses a content analysis of 113 public testimonies to guide a close reading of opponents' and proponents' statements. The spatial politics and overlapping concerns that drove negotiations tied to future gains and harms that may result from the Corridor.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| 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".