Vernacular Knowledge and Water Management - Towards the Integration of Expert Science and Local Knowledge in Ontario, Canada
Bibliographic record
Abstract
Complex environmental problems cannot be solved using expert science alone. Rather, these kinds of problems benefit from problem-solving processes that draw on 'vernacular' knowledge. Vernacular knowledge integrates expert science and local knowledge with community beliefs and values. Collaborative approaches to water problem-solving can provide forums for bringing together diverse, and often competing, interests to produce vernacular knowledge through deliberation and negotiation of solutions. Organised stakeholder groups are participating increasingly in such forums, often through involvement of networks, but it is unclear what roles these networks play in the creation and sharing of vernacular knowledge. A case-study approach was used to evaluate the involvement of a key stakeholder group, the agricultural community in Ontario, Canada, in creating vernacular knowledge during a prescribed multi-stakeholder problem-solving process for source water protection for municipal supplies. Data sources – including survey questionnaire responses, participant observation, and publicly available documents – illustrate how respondents supported and participated in the creation of vernacular knowledge. The results of the evaluation indicate that the respondents recognised and valued agricultural knowledge as an information source for resolving complex problems. The research also provided insight concerning the complementary roles and effectiveness of the agricultural community in sharing knowledge within a prescribed problem-solving process.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".