Communicating traditional environmental knowledge: addressing the diversity of knowledge, audiences and media types
Bibliographic record
Abstract
ABSTRACT Although there are a number of distinct audiences (for example students, hunter and trapper organisations, and co-management agencies) for traditional environmental knowledge, little work has been done in analysing how indigenous knowledge can be best communicated to these different groups. Using examples mainly from northern Canada and Alaska, we explore the challenge of collecting and communicating different kinds of traditional environmental knowledge; the media types or communication modes that can be used; and the appropriateness of these kinds of media for communicating with different audiences. A range of communication options is available, including direct interaction with knowledge holders, use of print media, maps, DVD/video, audio, CD ROM, and websites. These options permit a mix-and-match to find the best fit between kinds of knowledge, the intended audience, and the media type used. This paper does not propose to replace traditional methods of communication with technology. Rather, we examine how technology can serve community and other needs. No single option emerges as a clear best choice for communicating indigenous knowledge. Nevertheless, various media types offer avenues through which northern people can meet their educational, cultural, and political needs, and build cross-cultural understanding.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| 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".