Linking industry and ecology : a question of design
Bibliographic record
Abstract
Part 1: Introduction 1 Linking Industry and Ecology in Canada: A Question of Design / Ann Dale Part 2: Design and Ecology 2 Industrial Ecology as Ecological Design: Opportunities for Re(dis)covery / Nina-Marie Lister 3 Redesign as Deep Industrial Ecology: Lessons from Ecological Agriculture and Social Ecology / Stuart B. Hill 4 Industry in the City: From Industrial Ghettos to Eco-Parks / Jill Grant 5 Reworking Canadian Landscape and Urban Form through Responsive Urban Design: Healthy Housing and Other Lessons / Nik Luka Part 3: Industrial Ecology and Environmental and Business Management 6 Cleaner Production and Eco-Efficiency: Charting a Course for Sustainability / Nonita T. Yap 7 From Clusters and Networks to Islands of Sustainability / Raymond P. Cote and Heinz Peter Wallner 8 From Advanced Eco-Efficiency to Systemic Sustainability: What Leading Companies Are Doing and What Assistance and Pressure They Need from Governments and Other Players / Robert B. Gibson and Steven W. Peck 9 Mining, Minerals, and Sustainability / R. Anthony Hodge 10 Between Beckett's Trousers and Ecotopia: The Future of Industrial Ecology / James Tansey Part 4: Learning from Experience 11 Applied Industrial Ecology: Blue Box Recycling Lessons Learned and Implications for Canada's Greenhouse Gas Strategy / R.A. Flemington 12 Clustering for Sustainability: The Alberta Experience / Sumita Fons and Rebekah Young 13 From Waste Management to Industrial Ecology / Jonah Spiegelman Part 5: Conclusions 14 Opportunity or Illusion: The Vexed Promise of Industrial Ecology / John Robinson and Asoka Mendis Contributors Index
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.093 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".