Bibliographic record
Abstract
active welfare 133, 151-4, 175-83 advisory and support services, see formalization service agriculture 88, 201 amnesties society-wide 157-8 individual-level 158-9 Australia 44, 49, 53 Austria household services 87 sectoral distribution 89 size of underground economy 44, 53 awareness raising campaigns 185-95, 217-8 Bas-du-Fleuve 97 Belarus 49 Belfast 59, 98, 113, 201 Belgium Local Employment Agencies 175-6, 217 service vouchers 177-8, 217 size of underground economy 44, 53, 56 binary thought 21-2, 29-30 Bloom, John 26 Branson, Richard 26 British Chambers of Commerce 205 British Hospitality Association 205 building industry, see construction sector Bulgaria size of underground economy 49, 53 tax morality 95 business support services, see formalisation services business regulation, see regulatory compliance Business Volunteer Mentor Association 164 Cairo 74-5 Cambridge 201 Canada geographical variations 97 size of underground economy 44, 53, 56 voluntary disclosure 159 canary breeding 78 car maintenance 89 carpentry 80, 88 case studies established self-employed 81-5 fl edgling micro-entrepreneurs 1, 73-81 cash-deposit ratio approach 50-51 catering, see hospitality industry causes of underground economy 92-102 cheats, see dishonesty Chelmsford 201 Cheque Emploi Service, see France child-care 88, 89, 173-4 cockle-pickers 1, 199, 201 Confederation of British Industry 205 Commission for Racial Equality 205 community development fi nance initiatives (CDFI), see microenterprise development programmes compliance, see regulatory compliance construction sector 82, 87-8, 89, 167-71, 183, 201 Copenhagen 97 Coventry 201 Croatia, 53 Croydon 201 cultural traditions 95-6 CUORE initiative, see Italy currency demand approach 52-4 Cyprus 49
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 | 0.003 |
| 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 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.854 | 0.807 |
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".