Social movements thinking for managing change in large-scale systems
Bibliographic record
Abstract
Purpose This study explores the efficacy of social movements thinking for mobilizing resources toward sustainable change in large-scale systems such as health and social services. Design/methodology/approach The study proceeds from a critical realist perspective employing a qualitative multi-case study approach. Drawing on the tenets of grounded theory (i.e. constant comparative analysis and theoretical sampling), data from semi-structured interviews and field notes were analyzed to facilitate theoretical integration and elaboration. Findings One case study explores the emergence of social movements thinking in mobilizing a community to engage in sustainable system change. Data analysis revealed a three-stage conceptual framework whereby building momentum for change requires a fundamental shift in culture through openness and engagement to challenge the status quo by acknowledging not only the apparent problems to be addressed but also the residual apathy and cynicism holding the system captive to entrenched ideas and behaviors. By challenging the status quo, energy shifts and momentum builds as the community discovers shared values and goals. Achieving a culture shift of this magnitude requires leadership that is embedded within the community, with a personal commitment to that community and with the deep listening skills necessary to understand and engage the community and the wider system in moving forward into change. This emergent conceptual framework is then used to compare and discuss more intentional applications of social movements thinking for mobilizing resources for large-scale system change. Originality/value This study offers a three-stage conceptual framework for mobilizing community/system resources toward sustainable large-scale system change. The comparative application of this framework to more intentional applications of social movements thinking to planned change initiatives offers insights and lessons to be learned when large-scale systems attempt to apply such principles in redesigning health and social service systems.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".