Embedded Strategies for the Sustainability Transition - Setting Priorities and Goals Aligned with Systems Resilience
Bibliographic record
Abstract
It is time for companies to take a very different approach to corporate strategy. Our Embedded Strategies guide helps companies respond to the growing calls for businesses to articulate their purpose and their strategy in alignment with the need to shift the global economy towards the reduction of inequality, a rapid climate transition, the preservation of biodiversity, and the elimination of waste. This guide will help you to develop a contextual strategy and goals that ensure your company is doing its part to maintain the resilience of key social and environmental systems. Building on our Road to Context guide with insights from 300+ interviews with senior executives, CEOs, board chairs, and directors and our experiences supporting companies around the world, it outlines resources and tactics that help your company to scan for emerging issues and risks; understand their implications for your business; understand your impacts and your potential for positive influence; prioritise where it makes sense to direct your efforts; and set your strategy and goals in alignment with delivering systems value.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".