Bibliographic record
Abstract
abatement see exemption accountability 12, 33, 39, 88 administration, of property tax 41-47 centralization of 65, 231 in China 171-72 in Germany 98 in Guinea 208-9 in Hungary 226-30 in India 136-41 in Indonesia 121, 125 in Mexico 293 in Tanzania 192-3 in UK 84, 86-7 administrative costs 14, 64 in Hungary 233 nn.6, 10 in Indonesia 122 in Mexico 297 aerial survey, in Tanzania 193 agricultural land, taxation of 27, 34-5, 39-41 in Australia 94 in Canada 40 in Chile 288-9 in Germany 99, 101 in Guinea 205 in Hungary 226 in Japan 110, 113 in Kenya 187 n.4 in Mexico 294 in Philippines 39-40, 153 in Poland 255-6 in Russia 238, 239 in Tunisia 213, 215-6 in Ukraine 247, 249 see also farms; rural areas Alberta, 73 amnesty in Argentina 285 in Chile 290 annual (rental) value (ARV) see rental value appeals 44-5, 46 in Argentina 283 in Canada 74, 75 in Chile 290 in Colombia 270 in India 139, 140 in Japan 111 in Mexico 295 in Philippines 155 in Russia 244 in South Africa 202 in Tanzania 191 in Tunisia 216 in Ukraine 248 acquisition tax see transfer tax Andhra Pradesh 131, 136 appraisal in Hungary 228 in Indonesia 119, 122 in Tanzania 192 see also assessment; valuation area compared to market value, 30-31 as tax base 22-3, 26-7 in China 166 in Germany 104 in India 146 in Kenya 178-9 in Poland 254, 258 n.6 in Tunisia 27, 210, 214 in Ukraine 250-51 see also unit value Argentina 37, 281-5 arrears 8, 47, 48-9 in Argentina 284-5 in Canada 75 in Chile 289 in Germany 104, 106 n.7 in Hungary 229 in India 140-41 in Poland 255 in Russia 244 in Tanzania 197 n.10 in UK 85 Assam 136 assessed value/market value ratio 7 in Argentina 283
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.791 | 0.642 |
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".