Upholding diverse knowledge systems to identify marine areas of high ecological, socio-economic, and cultural value in Howe Sound/Atl'ka7tsem, British Columbia
Bibliographic record
Abstract
Marine spatial planning (MSP) involves bringing together knowledge associated with marine spaces and identifying strategies that proactively address pressures while protecting ocean-based values for present and future generations. Unfortunately, MSP processes are often biased toward biophysical and western scientific knowledge, and can exclude diverse ways of knowing (e.g. Indigenous and local knowledge systems) and socio-economic and cultural data. This oversight can not only hamper the effectiveness of MSP, but also generate conflict with communities whose access and livelihoods are impacted by MSP outcomes. In response to this issue, our study led a community-based approach to identify areas of high socio-economic, cultural, and ecological value, and bring together diverse ways of knowing to inform regional MSP in Howe Sound/A´tl'ka7tsem (one of three Squamish Nation place names). We implemented a mixed-methods participatory mapping approach that involved 36 semi-structured interviews and a survey of over 200 community members in A´tl'ka7tsem. We also conducted community-evaluation meetings to interpret and share the results. This research was led by a partnership between MakeWay Charitable Society, the Squamish First Nation, and the University of British Columbia. Importantly, we emphasized youth leadership and centred Indigenous voices throughout the planning, design, knowledge gathering and sharing processes. Our results identified complex interactions (e.g. compatibilities and conflicts) across values that are critical to informing MSP processes and stewardship in A´tl'ka7tsem. To improve data accessibility and close the knowledge-to-action gap, we shared our data with a publicly accessible online map - the Howe Sound/A´tl'ka7tsem Marine Reference Guide. Our process strengthened trust across diverse community members and identified regionally appropriate ways to decolonize marine planning and conservation processes. Overall, this study provides an inclusive framework for gathering knowledge that respects multiple ways of knowing and advances the capacity to protect both ocean and community health.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".