Bibliographic record
Abstract
How and why branded bottles of water have insinuated themselves into our daily lives, and what the implications are for safe urban water supplies. How did branded bottles of water insinuate themselves into our daily lives? Why did water become an economic good—no longer a common resource but a commercial product, in industry parlance a “fast moving consumer good,” or FMCG? Plastic Water examines the processes behind this transformation. It goes beyond the usual political and environmental critiques of bottled water to investigate its multiplicity, examining a bottle of water's simultaneous existence as, among other things, a product, personal health resource, object of boycotts, and part of accumulating waste matter. Throughout, the book focuses on the ontological dimensions of drinking bottled water—the ways in which this habit enacts new relations and meanings that may interfere with other drinking water practices. The book considers the assemblage and emergence of a mass market for water, from the invention of the polyethylene terephthalate (PET) bottle in 1973 to the development of “hydration science” that accompanied the rise of jogging in the United States. It looks at what bottles do in the world, tracing drinking and disposal practices in three Asian cities with unreliable access to safe water: Bangkok, Chennai, and Hanoi. And it considers the possibility of ethical drinking, examining campaigns to “say no” to the bottle and promote the consumption of tap water in Canada, the United States, and Australia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.360 | 0.167 |
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".