Methodological challenges in researching activism in action: civil society engagement towards health for all
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
Civil society engagement around health care and population health improvement is an important driver towards Health for All. Research can improve the effectiveness of health activism by examining the resources, structures and strategies of civil society engagement. However, research to support such engagement faces epistemological and methodological challenges which call for specific research strategies.A four year multi-country study was undertaken by the People’s Health Movement, a global network working for health for all. The research took place in six countries (Brazil, Colombia, DR Congo, India, Italy, South Africa) and globally, and was directed to understanding five domains of civil society engagement: movement building; campaigning and advocacy; capacity building; knowledge generation, access and use; and engaging with governance. The research plan and methods of data collection and analysis were tailored to address the objective of improving activist practice, while negotiating research challenges identified during the design phase.Results include insights into the practice of civil society engagement in relation to the five domains of activist practice, as well as experience gained in managing six methodological challenges which we describe as: making meaning, aligning research and action, managing power relations, valuing experiential knowledges, chaos and contingency, challenging preconceptions.Researching activism can produce useful insights into practice as well as support continuous improvement in the effectiveness of such activism. However, there are significant methodological challenges that can be addressed through appropriate strategies. More research, building on the approach described in this paper, can contribute to more effective civil society activism for health.
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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 | 0.036 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it