Exploring the Representation of Place-Based Knowledge in Spatial Planning in Nova Scotia, Canada
Bibliographic record
Abstract
Spatial planning is essential in the interdisciplinary management of dynamic coastal environments. However, conventional approaches to spatial planning do not focus on the comprehensive representation and visual communication of place-based knowledge (e.g., Indigenous and Local Knowledge systems). This oversight limits the contextual applicability of planning decisions. To understand this issue’s relevance in Nova Scotia, the suitability of six Decision Support Tools (DST) used in spatial planning for representing local perspectives was explored. Through a scoping review and semi-structured interviews with spatial planners, researchers, and users of coastal environments in Nova Scotia, key characteristics that make DST useful in representing place-based knowledge, as well as certain tool design limitations, were identified. Also identified were the generalized stages of the spatial planning process at which each of the selected DST are most effectively applied. The results are meant to inform the use and design of DST in a way that better account for and serve local coastal users throughout different stages of the spatial planning process, thereby supporting informed and equitable decision-making.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 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".