Wayshaping: A Multiscale Framework for Behavior Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.000 |
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 it