Bibliographic record
Abstract
Water Ethics: Think Like a Watershed is the fifth and final video developed for Urban Water: Innovations for Environmental Sustainability, a Massive Open Online Course (MOOC) that explores urban solutions to the global freshwater crisis, characterized by a dwindling supply of and access to freshwater. 1 Dr. Karen Bakker, the professor of the course, approached Lantern Films to create a series of videos on the topic of water and the built environment.Lantern suggested a film series that featured Vancouver-based innovations to water infrastructure and design; from a building that uses captured rainwater, to an energy utility that utilizes waste thermal energy to heat a neighbourhood, the videos focus on local urban innovations that reduce freshwater consumption.While the first four videos used in the Urban Water course are heavily focused on the built form -buildings, energy utilities, neighbourhood design, and creek regeneration -Water Ethics, featuring interviews with Secwepemc scholar Dr. Dorothy Christian Cucw'la7 and poet Dr. Rita Wong, asks us to critically think about our relationship with water.Dorothy Christian brilliantly reframes the discussion about water, moving away from a supply and demand framework to one based on ethics, rights, and relationality."When I think about ethics, for me it's a right relationship with the water.For me to be in right relationship with the water is to be sure that I am taking care of it, as it will take care of me" (Water Ethics, minute 1:04).Dorothy's words are a call for us all to resituate our connections with water by engaging in participatory water ethics: visiting where the water we drink comes from, understanding its flow, and acknowledging how it has been cared for.In the context of Vancouver, which is unceded Coast Salish territory, resituating our relationship with water also entails fundamentally ------------------------1The video discussed here -Water Ethics: Think Like a Watershed -may be accessed at https://vimeo.com/198392259
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.095 | 0.010 |
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".