Bibliographic record
Abstract
An environmentalist maps the hidden costs of overconsumption in a globalized world by tracing the environmental consequences of five commodities.The Shadows of Consumption gives a hard-hitting diagnosis: many of the earth's ecosystems and billions of its people are at risk from the consequences of rising consumption. Products ranging from cars to hamburgers offer conveniences and pleasures; but, as Peter Dauvergne makes clear, global political and economic processes displace the real costs of consumer goods into distant ecosystems, communities, and timelines, tipping into crisis people and places without the power to resist. In The Shadows of Consumption, Peter Dauvergne maps the costs of consumption that remain hidden in the shadows cast by globalized corporations, trade, and finance. Dauvergne traces the environmental consequences of five commodities: automobiles, gasoline, refrigerators, beef, and harp seals. In these fascinating histories we learn, for example, that American officials ignored warnings about the dangers of lead in gasoline in the 1920s; why China is now a leading producer of CFC-free refrigerators; and how activists were able to stop Canada's commercial seal hunt in the 1980s (but are unable to do so now). Dauvergne's innovative analysis allows us to see why so many efforts to manage the global environment are failing even as environmentalism is slowly strengthening. He proposes a guiding principle of "balanced consumption" for both consumers and corporations. We know that we can make things better by driving a high-mileage car, eating locally grown food, and buying energy-efficient appliances; but these improvements are incremental, local, and insufficient. More crucial than our individual efforts to reuse and recycle will be reforms in the global political economy to reduce the inequalities of consumption and correct the imbalance between growing economies and environmental sustainability.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".