Wayshaping: A Multiscale Framework for Behavior Change
Bibliographic record
Abstract
Habitual human behaviors shape nearly every aspect of life, from personal health and relationships to organizational success, disease transmission, and ecological sustainability. However, efforts to change behavior often fail to account for the complexity and multiscale nature of habit formation, leading to interventions that struggle to produce lasting effects. A persistent challenge is the intention-action gap, the discrepancy between what we intend to do and what we do in practice – an issue that traditional models of habit formation fail to fully explain. Here, we introduce the wayshaping framework, drawing on recent advances in cognitive science to emphasize the multiscale, complex and anticipatory nature of behavior. This framework makes three key contributions that significantly reframe how we understand and approach behavior change: (1) it reconceptualizes the individual as a multilevel, multiscale collective intelligence, offering a novel perspective on the organizing and developmental dynamics underlying habit formation; (2) it reinterprets the intention-action gap as a set of interdependent coordination challenges – non-linearity, alignment, and anticipation; and (3) it outlines principled skills for navigating these challenges and shaping habits in line with our intentions. By integrating insights from embodied cognitive science, complexity theory, behavior change research, and design, the wayshaping framework reframes individual habit change as a process of multiscale realignment. It thus provides a novel, unifying theoretical foundation for interdisciplinary research that has concrete and practical value in shaping sustainable behavior change.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".