International trade and its impact on biological diversity
Bibliographic record
Abstract
Introduction For the past twenty-five years, the world has been moving towards a free trade regime. At the same time the concern for the impact of free trade on natural resources is increasing. There is debate among the environmentalists and the economists on the impact of trade on welfare and biodiversity (see Chapter 18 of this volume). Environmentalists ‘worry that trade will expand the scope of market failures, put added strain on the environment and lead to degradation of natural resource stocks in the long run’ (Karp et al . 2001, p. 617), which in turn will decrease the welfare of both import and export countries. Many economists argue, however, that free trade will improve social welfare and rectify environmental externalities provided markets function efficiently, property rights over biodiversity resources are well defined and non-market values of natural resources are accounted for in the production process. The fact that most of the world's biodiversity-rich land lies in the populated and poor South makes the situation even worse. The biodiversity-rich South is already overburdened to meet the demand of its own population for biodiversity-derived goods (such as agricultural products, timber and non-timber forest products), while free trade, it is argued, adds further pressure to overuse and overexploit biodiversity resources. Yet as incomes grow in Northern countries, their consumers are displaying an increasingly stronger preference for so-called ‘green products’.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".