Bibliographic record
Abstract
This chapter analyzes past practices of global land use to propose a sustainable model of our relationship to the inhabitable Earth. Our Paleolithic ancestors first used fire to alter forest landscapes, contributing to the extinction of megafauna in the process. In the Neolithic, the invention of agriculture and the domestication of animals led classical commentators such as Plato to take note of soil erosion and soil degradation. The dawn of modernity and the European Age of Discovery, in addition to the genocide of peoples and cultures, has sent land use into overdrive. At the heart of our most recent ‘Green Revolution’ – the exponential increase in food production on a smaller per-capita land surface – lies a paradox. Not only has the application of synthetic fertilizers, pesticides and expanding monocultures caused widespread habitat destruction, degradation and loss of biodiversity, but more than 820 million people remain undernourished, despite this expansion. On the other hand, around 37% of the world population, mainly in the developed world, suffer from obesity as a result of excess calorific consumption, low in nutritional value. These inequalities point to a global imbalance in economic networks of production and consumption.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.271 | 0.122 |
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".