From Fracking Conflicts to Innovation Generation: a Case Study of Water Governance in Northeastern B.C.
Bibliographic record
Abstract
The Horn River Basin overlaps with the Fort Nelson First Nation (FNFN) traditional territory, and has been an active site of hydraulic fracturing development. This has increased the demand for water in the Basin. While it is well established that effective water governance requires collaboration from a wide array of actors, barriers to including Indigenous Nations in water governance remain as a legacy of Canada’s colonial history. The Province’s approach to involving Indigenous Nations in water governance has largely been limited to consultation and accommodation and slow government-to-government negotiations. This approach has yet to yield significant collaboration. Research partner, the Fort Nelson First Nations (FNFN) Lands Department, has been both formally and informally engaged in ongoing negotiations with government, and with industry on various issues related to the hydraulic fracturing and water use for hydraulic fracturing in the Horn River watershed. Governance innovation was needed to break the deadlock, and it was clear that a social learning process would be necessary if industry, government, and FNFN were going to establish a shared vision for future water governance arrangements. As part of their efforts, the FNFN Lands Department began a community consultation process to develop their own FNFN water management strategy, and sought research to better understand the range of possible organizational structures that would support a more acceptable governance arrangement. Therefore, this study aimed to explore the existing conditions for social learning in the Horn River Basin, support the FNFN approach to developing a water management strategy through research on social learning and community-based planning processes, and to examine possible alternative governance models.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".