Strategic analysis of the James Bay hydro-electric dispute in Canada
Bibliographic record
Abstract
A strategic analysis of the James Bay conflict was carried out by using the graph model for conflict resolution. In 1971, Hydro-Québec, which is the third-largest electrical generating company of North America, began its large-scale hydroelectric projects in the James Bay area. Since the projects would significantly affect the living conditions of the native people and the environment around that region, worldwide debates were stimulated. A conflict model was developed in terms of the decision makers, their options, and their preferences for the situation existing as of January 2002, just prior to the signing of the final agreement. Subsequently, a stability analysis based on the calibrated model indicates that a possible resolution is that Hydro-Québec reduces the number of proposed power stations to appease the native people, who in turn would not initiate lawsuits. Sensitivity and hypergame analyses were also carried out to demonstrate the effects of preferences of decision makers on the final resolution. In practice, the modelling and analysis were implemented using the decision support system, GMCR II®. Key words: Hydroelectric, conflict resolution, decision support system, graph model, stability analysis, sensitivity analysis, hypergame analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".