A Novel Framework for Inner-Outer Sustainability Assessment
Bibliographic record
Abstract
Calls for systemic transformations have become prevalent throughout sustainability discourse. Increasingly, these calls point towards consciousness expanding practices and interventions, such as mindfulness, to support the development of individual understandings, skills, and capacities that are conducive to more sustainable ways of being and doing. The growing interest in leveraging inner capacities, including mindsets, worldviews, values, and beliefs for sustainability transformations emerges from concerns that conventional approaches are failing to align social and ecological systems towards long-term viability. Interest in these consciousness-driven transformations is spreading, particularly in governments and prominent organisations. Tempering this enthusiasm are concerns that untethered from moral and ethical guidelines as well as caring understanding of local and global prospects for lasting wellbeing, mindfulness programs, workshops, and interventions for inner transformation can inadvertently strengthen unsustainable systems and deepen inequities. Accordingly, this paper presents an exploratory assessment framework to increase understandings of how events focused on interventions for inner transformation align with broad sustainability requirements. Findings from application of the framework should help to elucidate how these offerings can disrupt normative ways of thinking and doing, and in turn, positively influence multi-scalar transformations. Furthermore, use of the assessment process to plan and/or evaluate inner development offerings is anticipated to help strengthen progress towards sustainability and reduce adverse trade-offs that might undermine positive systemic transformations.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".