Sustainability-focused Organizational Learning: Recent Experiences and New Challenges
Bibliographic record
Abstract
To an increasing extent, corporations and smaller businesses are making explicit commitments to improved environmental and social performance. Some have embraced the goal of sustainability, and some prefer to use the term 'triple bottom line'--a balance of financial, social and ecological performance--in their operations. Some companies are experimenting with organizational learning as a means to accelerate the transition to sustainability or the triple bottom line. This fledgling combination--sustainability and organizational learning--is the focus of this paper. The term 'sustain ability-focused organizational learning' (SFOL) is proposed to describe the early experience of companies that are attempting to pursue sustainability or the triple bottom line while making substantial changes to their organizational cultures. In many instances, these changes involve the use of experimental or unconventional learning techniques. Some companies are combining their SFOL efforts with The Natural Step, a sustainability framework. The experience of five companies pursuing SFOL is summarized and analysed in a non-identifying way, and key preliminary lessons are discussed.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".