Bibliographic record
Abstract
Part 1: Introduction 1. Footprints and Fair Earth Share. Bill Rees and Jennie Moore Part 2: What does Living within a Fair Earth Share Mean? 2.1: Personal Footprint 2. Food. James Richardson 3. Domestic Travel. Robert and Brenda Vale 4. Consumer Goods. Maggie Lawton 5. The Dwelling. Nalanie Mithraratne 6. Tourism. Abbas Mahravan 2.2: Collective Footprint 7. Infrastructure. Ning Huang 8. Government. Jeremy Gabe and Rebecca Gentry 9. Services. Soo Ryu Part 3: Footprints in the Past 10. A Study of Wellington in the 1950s. Carmeny Field (with Brenda Vale) Part 4: Footprints in the Present 11. A Study of China. Yuefeng Guo 12. A Study of Suburban Thailand. Sirimas Hengrasmee 13. Kampung Naga, Indonesia. Grace Pamungkas (with Brenda Vale and Fabricio Chicca) 14. A Study of Hanoi, Vietnam. Han Thuc Tran 15. A Study of Suburban New Zealand. Sumita Ghosh 16. The Hockerton Housing Project, England. Brenda and Robert Vale 17. Education for Lower Footprints. Sant Chansomsak 18. Footprints and Income. Ella Lawton 19. Sustainable Urban Form. Fabricio Chicca Part 5: Conclusions 20. I wouldn't start from here... Robert and Brenda Vale
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.027 |
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".