Blue ecology: A cross-cultural approach to reconciling forest-related conflicts
Bibliographic record
Abstract
Fresh water has a unifying role at the ecosystem and human level. Water, without fail, is recognized throughout the globe as crucial to human life. By examining a dispute resolution case study relating to Mt. Ida, near Salmon Arm, B.C., this paper offers a probe of the question “What is water?”. An Elder poses three questions about fresh water's role in the forest ecosystem; the answers are sought using the concept of “blue ecology,” which interweaves Traditional Ecological Knowledge (TEK) and Western science. The purpose is to reveal cross-cultural assumptions and definitions of fresh water, and to assist in reconciling forest-related conflicts between First Nations and government agencies. Because water is a common interest to all people, blue ecology is proposed as a means towards this reconciliation. The paper presents five guiding principles that should be useful to mediators and forests managers seeking to build co-operative cross-cultural solutions.
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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.049 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| 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".