Bibliographic record
Abstract
57-58, 68-69 Barton Underpass Mural Project (BU MP), 63, 68 Black, Asian and minority ethnic population (BAME population), 127 Black lives matter, 19 Blame games, 130 Brazil, 8, 34 public participation in, 38 Bus rapid transit systems (BRT systems), 143, 147 Change, 4-9 City Growth Agreements (CGAs), 139, 146 City logistics, 79-81, 83-84, 88, 90-91 efficiency, 81 policies, 83 stakeholders, 91 Civic participation, 4, 9, 98-99, 105-106, 109, 111, 171 Civic roads, 98, 100, 102, 105, 109 associations, 98, 100-101, 105, 111 in Sweden, 100-103 Civil society, 35, 39, 99, 101, 110 Coalition for Humane Immigrant Rights of LA, 21 Collaborative planning, 18, 35, 120, 184 Collective action, 21, 47, 78, 140-141, 147-149 Commoning as theory, 99-101 Commoning roads, 105 civic participation, 105-106 commoner, 108-109 Communicative challenge, 138, 140-141, 148-151 Community Based Participatory Research (CBPR), 56 Community benefits agreements (CBAs), 21, 24 Computer-Assisted Telephone Interviews (CATI), 87 Congestion tax, 167 COVID-19 pandemic, 20, 62, 79, 121, 172, 187 Department for Transport (DfT), 118, 121-122, 129 Direito de ir e vir, 45 Economic Opportunity Act (1964), 17 Edinburgh, 164, 176 road pricing in, 165-168 tram lines in, 168-170 Emergency Active Travel Fund (EATF), 118-119, 121, 130 implementing, 123-126 England, 56, 131, 158 Europe, 118, 138, 143, 170 SUM planning in, 165 Favela Santa Marta, 8, 34, 38 Free transit Toronto (FTT), 19
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.802 | 0.831 |
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".