Bibliographic record
Abstract
adivasis (original inhabitants) 120 adjustment lending 308-10, 312 Adjustment Lending Retrospective (2001) 310 affected community 4, 6-7, 14, 17, 23, 44, 46, 62, 79-80, 82, 86, 90, 142, 194, 239, 247, 292, 302-4 African Development Bank 309 agriculture and tree plantation industries, residential location of workers in 181 Aim for Human Rights, in the Netherlands 361-3 Alberta Oil Sands Developers Group, Canada 209 alternative dispute resolution (ADR) 276 Anglo Ferrous Brazil 245 apartheid 38, 39, 42, 55 Argyle Participation Agreement (Australia) 64-8, 74 artisanal and small-scale mining (ASM) 296 Ashoka Open Cast Project (OCP), India 124-5 Asociación de los Andes de Cajamarca 246 aspirations 10, 11, 14, 74, 120, 143, 237, 242, 263 Australia Aboriginal community 65-6 Argyle Diamond Mine participation agreement 64-8, 74 Australian Bureau of Statistics (ABS) 328, 332, 336 Department of Families, Youth and Community Care 283 Emergency Management Australia
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.535 | 0.348 |
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".