Sustainability innovations in industry-academy collaborations
Bibliographic record
Abstract
Sustainability innovations in industry-academy collaborations: An interview with Tima Bansal. Pratima (Tima) Bansal is a Professor of Sustainability Strategy at the Ivey Business School, Ontario, Canada, with previous visiting appointments at, and affiliated with, the University of Cambridge, MIT, and Monash University. She is the Executive Director and Founder of the Network for Business Sustainability, a growing network of over 35,000 followers from management research and practice. The network is committed to advancing sustainable business, and Innovation North, a collaboration of researchers and managers seeking to innovate the corporate innovation process to embrace systems thinking. She also heads the Ivey Innovation Learning Lab, which helps businesses create value for themselves and society simultaneously over the long term. She chairs the Canadian Council of Academies Expert Panel on the Circular Economy, vice-chairs and sits on the Boards of the United Nation’s Principles for Responsible Education, and sits on the Board of Governors and of the Academy of Management.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".