Bibliographic record
Abstract
Agence de l'environment et de la Maîtrise de l'Energie) 262 administrative bodies, commissioning of evaluations 54 administrative structures, SD, France 261-3 agri-environmental programme committee (Austria) 113 agri-environmental programmes 110 case study, Austria 111-12 conclusions 120-21 evaluation approach and identification of indicators 112-17 results 118-20 agriculture sector, French national strategy 266 alternatives, defining, SIAs 92-3 Amsterdam Treaty (1997) 294 analysis, NSDS evaluation 104 Analysis of Context, US environmental reports 313 annual reports, French national strategy 268-9 applied sciences, challenges for 321-3 assistant review leader, PRESUD project 193 Austria agri-environmental programme 111-12 case study conclusions 120-21 evaluation approach and identification of indicators 112-17 results 118-20 development of SEA and SIA 84-9, 94 Austrian Environmental Impact Assessment (EIA) Act (1994) 84 basic workshop, self-evaluation 223-6 Belgium, institutionalized evaluations 237-42 federal policy and planning cycle 238-9, 242 impact assessment processes 240-41 best timing, evaluation efforts 322 'Better regulation' policy agenda 240 biennial reports, Belgium Federal Planning Bureau 238-9 Biodiversity Convention 98 biophysical process models, agrienvironmental programme analysis 117 Brundtland Report (1987) 1-2, 4, 43, 63, 126 budgetary provision, NSDS evaluation 104 Bush, George W. 308 Canada, SEA and SIA 88 capacity-building, Finnish evaluation reports 289 capitals' model see four capitals' model case studies, successful development of SIA 94 Cells for Sustainable Development 241 Central and Eastern Europe, evaluating governance-related technical assistance projects 201-13 CIDA 203, 206, 207, 209, 211 cities, local authority organization, France 263 citizens, French national strategy 266 city coordinators, PRESUD project 195 City of Tomorrow and Cultural Heritage 181
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 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.566 | 0.389 |
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".