The "Greening the Ivory Towers" Project: The University of Auckland case study
Bibliographic record
Abstract
Dr Tom Fookes is an Associate Professor in Planning at the University of Auckland, New Zealand. He is a member of the Wortd Society for Ekistics and a graduate of the Athens Center of Ekistics. He arranged an undergraduate Bachelor of Planning student project on Greening University Campuses with the students travelling to Toronto for the Natural City Symposion where they reported on their work with posters and in a formal presentation. The principal student presenters were Alison Hall and Logan Whitelaw in conjunction with Nicola Bishop, LLoyd Johnston, Karen Kao, and Michelle Lee, Bplan students in the Department of Planning, University of Auckland. The text that follows is based on a PowerPoint presentation at the international symposion, 23-25 June, 2004, sponsored by the University of Toronto's Division of the Environmental Studies, and the World Society for Ekistics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".