143Chapter 9 Measuring and training Inner Development Goals: Developing inner drivers for achieving sustainability goals
Bibliographic record
Abstract
Organizations worldwide are pledging ambitious climate action, but translating these commitments into meaningful progress remains a significant challenge. This chapter introduces an approach focused on Inner Development Goals (IDGs) – measuring and training the critical inner capacities that enable individuals to become effective agents of environmental change within their own lives and within organizations. Through an insightful narrative, the chapter chronicles the journey of developing the Inner Development Goals – Adapted (IDG-A) scale to rigorously assess 29 personal skills, qualities, and mindsets across six key dimensions: self-awareness, cognitive skills, caring for others and the world, social skills, driving change, and feelings of organizational belonging. Research validates the IDG-A in evaluating employees’ readiness for climate action. Moreover, the chapter presents the meaning, awareness, and purpose (MAP) model – an experiential coaching process designed to activate teams by connecting them deeply to the meaning behind sustainability efforts, fostering collective awareness, instilling a shared sense of purpose, and translating their inner work into consistent outward impact. Findings show the MAP model’s benefits in elevating IDG-A scores and catalyzing measurable climate initiatives compared to conventional training approaches. When focusing on human inner drivers as the catalyst for organizational outer change, this chapter offers a framework for addressing a critical impediment: the intrinsic motivation required from employees to fully embrace their role in achieving an organization’s environmental goals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.027 |
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".