Organizational Ecology and Culture Change as Policy Tools for Attaining Sustainability in Business
Bibliographic record
Abstract
This paper focuses on two process tools that can be used to promote sustainability in organizations, organizational culture change and organizational ecology. These tools offer unique toolsets for companies in creating long-term environmental sustainability. The paper utilizes the Yin Case study method, focusing on the Canadian telecommunications company Telus Inc and international food supplier The Kraft Heinz Company. This paper primarily uses document and case study analysis. Culture change has been a successful tool for Telus Inc in improving its environmental sustainability and its model could be replicated by other companies to improve their own. Organizational ecology may be difficult to employ but Eric Trist’s version offers a complementary model to the social purpose business model. Future research is needed to determine how this version of organizational ecology could specifically be used to improve a company’s environmental sustainability. Both tools have potential to give companies adaptable and formidable methods of increasing internal and external environmental sustainability within their organizations. In addition, if utilized fully, these processes would improve the overall competitiveness of these organizations as seen with Telus Inc and its decade long culture initiative.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".