Bibliographic record
Abstract
In RECENT YEARS, critical discussions of consumption and consumerist lifestyles, long consigned to the academy and the counter-cultural fringe, have increasingly moved to the forefront of some of the most urgent social, political, and economic debates of our time. Contemporary environmentalists, for instance, have consis tently attempted to sound the alarm that our accelerating levels of consumption and resource-use are bringing us to the verge of ecological exhaustion. As Betsy Taylor and David Tilford argue, all available evidence suggests that skyrocketing con sumption is rapidly depleting the Earth's ecosystems, robbing future generations of vital life-sustaining resources... [and] using far more of the Earth than the Earth has to offer.1 Indeed, Alan Durning writes, measured in constant dollars, the world's people have consumed as many goods and services since 1950 as all previous gen erations put together.2 As a consequence of such dramatically inflated rates of consumption, the World Wildlife Fund reports, global ecosystems over the past 25 to 30 years alone have lost over 30 per cent of the basic resources needed to sustain life on this planet.3 Such aggregate global measures, of course, don't really do justice to the stark discrepancies in consumption between the global North and South that are an inte
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 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".