Harnessing Digital Storytelling with Maps to Empower Coastal Communities for Marine Stewardship and Conservation
Bibliographic record
Abstract
Coastal communities play a vital role in marine stewardship and conservation efforts, as they directly interact with and rely on the oceans for their livelihoods and cultural identity. The effective communication of these communities' experiences, values, and challenges is crucial for fostering collective action amidst the pressures from changing coastlines. Storymapping, a combination of storytelling and interactive mapping technology, offers a compelling approach to create narratives that illuminate the intricate connections between people, places, and marine ecosystems. By seamlessly integrating local knowledge, cultural heritage, and environmental spatial data, storymaps provide a platform to share unique experiences, promote sustainable practices, and raise awareness about the importance of their neighbouring ecosystems. Drawing on a series of case studies from Atlantic Canada, this presentation explores the key advantages of utilizing dynamic and static maps, emphasizing their ability to enhance community engagement, foster stakeholder collaboration, and facilitate informed decision-making processes. By capturing and preserving these valuable aspects of community heritage, such as oral histories, local knowledge, and Indigenous practices, storymapping approaches can help inform local audiences and enable them to actively participate in the future of their coastal environment. Prepared for Coastal Zone Canada 2023 Conference.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".