Bibliographic record
Abstract
Figures 2.1 Bulgaria -BNB's foreign assets, monetary base and credit to the private sector (million leva) 44 A2.1 Lithuania -credit to the private sector (million litai) 59 A2.2 Bulgaria -BNB's foreign assets, monetary base and credit to the private sector (million leva) 60 A2.3 Bosnia and Herzegovina -CBBH: foreign assets and monetary liabilities (million KM) 60 4.1 Correlation of Mexican and US business cycles before and after NAFTA 85 4.2 Correlation of Canadian and US business cycles before and after NAFTA 86 4.3 Correlation of Canadian and Mexican business cycles before and after NAFTA 88 4.4 Exports and imports 88 5.1 Real peso/dollar exchange rate 96 5.2 Real Mexican wage 98 5.3 Real Mexican wage (seasonally adjusted) 99 5.4 Real US wage (seasonally adjusted) 100 6.1 Evolution of Canadian dollar in US funds 122 6.2 Canada-US nominal and real interest rate spread, 1946-2001 123 6.3 Evolution of real interest rate spread between core EMS countries and USA (1965-99) 125 6.4 General government primary balances 126 7.1 US dollar price of one Canadian dollar 134 7.2 Purchasing power of total real national income per adult, Canada as a percentage of the USA (1970-98) 141 9.1 Announced asset-backed bonds in the international markets (US$ billions) 180 9.2 Private credit from deposit money banks to GDP -selected developed and developing economies (1997) 181 9.3 Interest rates margins in selected developed and developing economies (1997) 181 9.4 Some indicators of the size of securities in selected developed and developing economies (1997)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.834 | 0.732 |
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".