Bibliographic record
Abstract
One of the biggest challenges for global environmental governance is “the problem of consumption.” The task involves far more than simply influencing what consumers choose, use, and discard. It requires a concerted effort to address the systemic drivers—including advertising, economic growth, technology, income inequality, corporations, population growth, and globalization—that shape the quantities, costs, and distribution of consumer goods. Current efforts to green consumption are “improving” management on many measures, such as per unit energy and resource use. Yet, this essay argues, such “progress” needs to be seen in the context of a rising global population and rising per capita consumption, where states and companies displace much of the costs of consumption far from those who are doing most of the consuming. This raises many questions about the value of sub-global measures for evaluating the environmental effectiveness of efforts to govern consumption. It also suggests the need for more global cooperation to mitigate the ecological effects of consumption. Current international initiatives such as the Marrakech process to draft a 10-Year Framework on “sustainable production and consumption,” however, will need to go well beyond simply promoting efficiencies, new technologies, and a greening of household consumption. Researchers in global environmental politics can assist here by probing even further into the complexity of governing the drivers and consequences of consumption, then working to thread these findings into the international policy process.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".