Bibliographic record
Abstract
Absolute b-convergence, 28, 48, 255 FDI, 62 LER, 62 per-capita gross domestic product (PCGDP), 62 Adaptation Fund (AF), 202 Adjusted R-squared (ARS), 292 Aggregating function, 37 Agricultural Development Corporation (ADC), 120-121 Agricultural gross domestic product (AGDP), 286 Agriculture, 288, 321 income, 118 India, 289-291 productivity of, 289-291 trade, 162 Air pollution, 82-83 Akaike information criterion, 216 America First, 184 ANOVA test, 86 ARDL.See Autoregressive distributed lag (ARDL) Augmented Dickey Fuller (ADF) Test, 256 Autoregressive distributed lag (ARDL), 49, 55 New Zealand, 199 Climate Change Program, 198-199 Climate Investment Fund (CIF), 202 Cocoa expansion, 121 CO 2 emissions, 3-4, 132-133, 154, 158 Cointegration, 53, 55, 156, 206, 208, 213, 215-216 outcomes of, 158 Companies Act, 342-343, 2013 Composite indicator, 32 KOF Globalization Index, 33 macroeconomic performance, 33 TOPSIS methodology, 33 Compound Annual Growth Rate (CAGR), 254, 258-259 Conditional b-convergence, 28, 48 FDI, 62 LER, 62 per-capita gross domestic product (PCGDP), 62 Construction industry (CI) bioeconomy, 224-225 material and processes complying with, 228-229 post-carbon transition, 225-227 value chains in, 226-227 Contingency coefficient, 124 Convergence, 27, 29 absolute, 48, 255 Bangladesh, 59-60 beta (b), 59, 256, 258, 261 Caribbean, 50 conditional, 29, 48 conomic growth and, 58 definitions of, 48 European Union, 50 globalization, 48 income, 48-49 Indian Ocean Zone (IOZ), 50 Latin America, 50 Least Square method, 61 in per capita incomes (PCIs), 252 Ramsey's model, 59 relative, 48 sigma, 59, 256, 258 stochastic, 256
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.701 | 0.735 |
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".