Bibliographic record
Abstract
Tables 1.1 Case study matrix 15 1.2 Main concepts 18 2.1 Country indicators 24 3.1 Selected statistics on health expenditures and prevalence of diabetes in case study countries 69 3.2 Percentage of uninsured by race/ethnicity in the United States (persons under 65 years old) for 2001-11 80 4.1 Evolution of maize production in Argentina, selected years 110 5.1 Summary of the major mobile phone service providers by country (2012) 128 5.2 Summary of mobile phone subscription levels by country (2011) 136 5.3 Summary of proportion of pre-paid users by country (2011 or most recent year where data was available) 141 7.1 Technological projects, actors, and public interventions 196 8.1 Summary of findings (Argentina) 207 9.1 Summary of the technological projects and their distributional consequences in Canada 220 10.1 Summary of the patterns between the technologies and structural, distributional and representational inequalities (Costa Rica) 233 11.1 Summary of findings from the technology/country case studies (Jamaica) 240 13.1 Summary of the technological projects and their distributional consequences in Malta 266 14.1 Assets, costs, benefits and employment (Mozambique) 290 14.2 Summary of the relation of the four studies technologies with (in)equality (Mozambique) 292 14.3The components of the science and technology innovation system of Mozambique crossed with the four researched technologies 296 14.4 Alternative policies (Mozambique)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".