Designing Ocean Futures Literacies: Reimagining the shoreline cleanup as a tool for ‘amphibious thinking’
Bibliographic record
Abstract
In an era where data proliferation often substitutes for genuine understanding, this workshop challenges participants to explore alternative modes of environmental engagement beyond traditional knowledge acquisition. We invite participants to question: How does knowing something truly catalyze change? And more critically, how might we move beyond the paradigm of data-driven knowledge to embrace more nuanced, collective approaches to environmental stewardship? Drawing from our ongoing research on Community Ocean Futures: Activating Data for Eco-social Change, in Vancouver, Canada, this workshop introduces participatory methods for engaging with marine environments and ocean conservation. Our approach deliberately moves away from conventional citizen science initiatives that prioritize data collection and quantification, instead emphasizing what we term "amphibious thinking": a mode of engagement that embraces precarity, multiplicity, and collective imagination.
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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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".