Bibliographic record
Abstract
In laymen’s terms, recycling is “good for the environment.” It involves “doing your bit” to help “save the Earth.” Yet, recycling requires high expenditures of energy and virgin materials, and produces pollutants, greenhouse gases and waste; it creates products that are “down-cycled” because they are not as robust as their predecessors, nor are such products usually recyclable themselves. Of the fifteen to thirty percent of recyclables that are retrieved from the waste stream, “almost half” are buried or burned due to contamination or market fluctuations that devalue recyclables over virgin materials (McDonough and Braungart, 56-60; Rogers, 176-179; Luke, 115-135; Rathje, 203-7;MacBride; Ackerman; EPA; Grassroots Recycling Network, Taxpayers for CommonSense, Materials Efficiency Project and Friends of the Earth). Furthermore, recycling infrastructure creates a framework where disposables become naturalized commodities instead of allowing practices of waste redesign, reduction or elimination. How is the schism between the popular perception of recycling as “good for the environment” and its less environmentally sound industrial processes maintained? By critiquing the visual culture of recycling campaigns, I argue that the meaning of recycling has been decontextualized, narrowed, and naturalized, thus functioning as a commodity sign.That is, recycling has been “abstracted from [its] context and then reframed in terms of the assumptions and interpretive rules of the advertising framework” through which it is promoted (Goldman, 5). I identify three main characteristics of the recycling commodity-sign. First, the individual, rather than government or industry, is represented as the primary unit of social change. Secondly, recycling is depicted as an act that ends at the blue bins, cutting out the industrial side of the cycle. Finally, recycling is symbolized as something that benefits the environment “in general” rather than as a specific form of waste management. Overall, I argue that recycling, instead of being a solution to environmental or waste crises, in fact constitutes a crisis of meaning that allows environmental degradation and derisory waste practices to continue.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.116 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".