On Using Expert-Based Science to “Test” Local Ecological Knowledge
Bibliographic record
Abstract
The challenges and opportunities of incorporating information collected through scientific studies with the experience-based knowledge of resourcedependent communities have been the focus of numerous studies (e.g., Freeman 1992, Agrawal 1995, Weeks and Packard 1997, Turner et al. 2000). However, there are relatively few examples in which ecological science and local knowledge have both been successfully incorporated to provide meaningful input into resource management (Berkes 2004). In their recent article in Ecology and Society, Gilchrist et al. (2005) provide a thorough evaluation of Local Ecological Knowledge (LEK) using expert-based ecological studies often referred to as “western science.” Although we applaud their recognition of the value of and desire to promote LEK, it is unfortunate that they use expert-based ecological data as a “test” to determine the “reliability” of LEK. Even though the authors indicate their wish to use the two different approaches to identify “constraints and limitations of both approaches,” they fail to discuss the assumptions, limitations, or constraints of the ecological studies that they use. We do not take issue with their ecological studies; we presume they are of the highest quality. However, to assume that the ecological studies are error free and without any bias or limitation is perhaps somewhat misguided, albeit an assumption that many scientists still make (Harding 1991, Rykiel 2001). Indeed, Freeman (1992) provides examples in which conflicts occurred in the Canadian Arctic between LEK and expert-based science over aerial surveys of bowhead whales in the Beaufort Sea and caribou in what is now Nunavut, where local perceptions of the state of these wildlife populations were initially considered “unreliable” but were resolved when biases in ecological studies were corrected using local knowledge. These case studies illustrate the limitations of ecological research and monitoring, and provide a cautionary tale against accepting them as “truth.”
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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 | 0.139 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.023 | 0.042 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".