Preparing an Organization for Sustainability Transitions—The Making of Boundary Spanners through Design Training
Bibliographic record
Abstract
Organizations today need to adapt their operations for a more sustainable future, and the transition management literature has highlighted the need for individuals who can collaborate with others to find new paths forward. Essentially, these individuals are boundary spanners with specific skills and competences to bridge diverging perspectives and facilitate knowledge dissemination and integration. Such individuals become critical change agents in organizations and essential in preparing the organization for sustainability transitions. The purpose of this study is to explore how organizations can enable and encourage individuals to take on this role and develop the skills and competences needed to become boundary spanners. Based on a case study set in a large Canadian energy corporation striving to shift towards more sustainable operations, our paper explores the emergence of boundary spanners, focusing on the effects of a design training program in supporting such roles in the organization. Our findings outline essential characteristics of boundary spanners; through illustrative career trajectories of four individuals participating in the training program, we show how the training program contributed to the emergence of boundary spanners.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".