Methodological challenges in researching activism in action: civil society engagement towards health for all
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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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 | 0.364 | 0.490 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".