Evaluating the efficacy of GIS maps as boundary objects: unpacking the limits and opportunities of Indigenous knowledge in forest and natural resource management
Bibliographic record
Abstract
The meaningful inclusion of diverse forms of knowledge, such as Indigenous knowledge (IK), remain unrealized in many natural resource management decision-making processes. Innovative boundary objects could be used to facilitate the effective inclusion of IK in natural resource management decision-making processes. In this study, Geographic Information Systems (GIS) maps were used as boundary objects due to their ability to visually display IK across knowledge boundaries. Using a conceptual framework that combines the Six Faces of Traditional Ecological Knowledge (TEK) outlined by Houde (2007). “The Six Faces of Traditional Ecological Knowledge: Challenges and Opportunities for Canadian Co-Management Arrangements.” Ecology and Society 12 (2): 34–50. http://www.ecologyandsociety.org/vol12/iss2/art34/) and boundary object criteria derived from the boundary science literature, our study investigated whether and how GIS maps could be used to increase the influence of IK on forest management. The four boundary object criteria (interpretive flexibility, accommodating concreteness, facilitating joint process, and satisfying information need) generated insight into specific ways to reduce the current barriers that may restrict greater use of IK within GIS and allow them to function more effectively as boundary objects.
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 | 0.136 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".