Bibliographic record
Abstract
analytic hierarchy process (AHP) 43 Argentina country profile of 140 economy of 140 Asian Financial Crisis (1997-98) 171 Australia 18, 121, 123 country profile of 141 natural resources of 141 trade policy of 141 Austria 123 country profile of 142 economy of 142 Bangladesh country profile of 143 economy of 143 Belgium 16, 123 country profile of 144 economy of 144 Brazil 182 country profile of 145 economy of 145 GDP per capita 145 Business Competitiveness Index (BCI) 25 Cambodia 18, 121 country profile of 146 economy of 146 Canada 16, 18, 121, 199 country profile of 147 trading policies of 147, 174 Chile 17, 30 country profile of 148 economy of 148 trading policies of 148 China 16, 31, 129-30, 148, 161, 193 agricultural and industrial output of 149 country profile of 149 economy of 127, 149, 164, 168 increase of national competitiveness 128-9 cluster analysis concept of 132 Colombia 182 country profile of 150 economic policies of 150 corruption 9, 39, 65, 67, 69, 105, 107, 143, 152, 164-5, 170, 178, 187, 207, 290, 294 fighting 175, 197 high levels of 185 Cost Strategy Index (CSI) 47, 61 correlations in 46 Cost Strategy Index -Neutral Strategy Index (CSI-NSI) 46 created advantage 17, 133-5 concept of 131-2 relationship with inherited advantage 136-8 Croatia country profile of 151 economy of 151 War of Independence (1991-95) 151 currency devaluation as means of enhancing national competitiveness 58 Current Competitiveness Index (CCI) 25 Czech Republic country profile of 152 economy of 152 EU accession of (2004) 152 Denmark 16, 123 country profile of 153 economy of 153 Differentiation Strategy Index (DSI) 47, 61 correlations in 46 Differentiation Strategy Index-Cost Strategy Index (DSI-CSI) 47
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 | 0.006 |
| 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 | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.300 | 0.165 |
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".