Linking forests and economic well-being: a four-quadrant approach
Bibliographic record
Abstract
This paper has three main objectives: (i) to investigate whether the four-quadrant approach introduced by J.S. Maini reveals a useful typology for grouping countries by gross domestic product (GDP) and forest cover per capita, (ii) to determine if the framework can enhance our understanding of the relationship between forest cover and GDP per capita, and (iii) to investigate why countries in the four-quadrant world occupy different quadrants and to determine the principal factors affecting country movement across and within the individual quadrants. The examination reveals that countries can be classified into four broad categories and that GDP and forest cover per capita have a low but consistent level of negative association. After regressing economic, institutional, social capital, and other variables on a country’s occupancy and movement in the four-quadrant world, the results suggest that countries in each quadrant share different characteristics and that factors underlying country movement vary according to the quadrant being observed. Overall, countries with less corruption and higher education are likely to experience increases in both forest cover and GDP per capita, while countries exporting a significant proportion of forest products have a reduced probability of increasing both variables.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".