Accessing Local Tacit Knowledge as a Means of Knowledge Co-Production for Effective Wildlife Corridor Planning in the Chignecto Isthmus, Canada
Bibliographic record
Abstract
Inclusive knowledge systems that engage local perspectives and social and natural sciences are difficult to generate and infuse into decision-making processes but are critical for conservation planning. This paper explores local tacit knowledge application to identify wildlife locations, movement patterns and heightened opportunities and barriers for connectivity conservation planning in a critical linkage area known as the Chignecto Isthmus in the eastern Canadian provinces of Nova Scotia and New Brunswick. Thirty-four local hunters, loggers, farmers and others with strong tacit knowledge of wildlife and the land participated in individual interviews and group workshops, both of which engaged participatory mapping. Individuals’ data were digitised, analysed and compiled into thematic series of maps, which were refined through participatory, consensus-based workshops. Locations of key populations and movement patterns for several species were delineated, predominantly for terrestrial mammals and migratory birds. When comparing local tacit-knowledge-based maps with those derived from formal-natural-science models, key differences and strong overlap were apparent. Local participants provided rich explanatory and complementary data. Their engagement in the process fostered knowledge transfer within the group and increased confidence in their experiential knowledge and its value for decision making. Benefits derived from our study for conservation planning in the region include enhanced spatial data on key locations of wildlife populations and movement pathways and local insights into wildlife changes over time. Identified contributing factors primarily relate to habitat degradation and fragmentation from human activities (i.e., land use and cover changes caused by roads and forestry practices), thereby supporting the need for conservation measures. The generated knowledge is important for consideration in local planning initiatives; it addresses gaps in existing formal-science data and validates or ground truths the outputs of existing computer-based models of wildlife habitat and movement pathways within the context of the complex social-ecological systems of the place and local people. Critically, awareness of the need for conservation and the value of the participants’ shared knowledge has been enhanced, with potential influence in fostering local engagement in wildlife conservation and other planning initiatives. Consistent with other studies, engagement of local people and their tacit knowledge was found to (i) provide important insights, knowledge translation, and dissemination to complement formal, natural science, (ii) help build a more inclusive knowledge system grounded in the people and place, and (iii) lend support to conservation action for connectivity planning and human-wildlife co-existence. More broadly, our methods demonstrate an effective approach for representing differences and consensus among participants’ spatial indications of wildlife and habitat as a means of co-producing knowledge in participatory mapping for conservation planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".