Grassroots organisations and the sustainable development goals: no one left behind?
Bibliographic record
Abstract
Walter Flores and Jeannie Samuel argue that grassroots organisations are essential to ensure improvements in the health of marginalised populations A recent UN report indicates that local involvement with the sustainable development goals (SDGs) remains nascent at best in many countries.1 Grassroots organisations have a critical role in advancing progress towards the goals, especially at a subnational level.2 Nonetheless, these groups remain a largely untapped resource. Ensuring that “no one is left behind” is a commitment of the 2030 SDG plan. The SDGs seek not only to achieve national outcomes but also to reduce inequalities within countries. This is no small task. Often these inequalities have become normalised in state institutions. Provision of lower quality social services to geographically, economically, and socially marginalised populations is often seen.3 However, the SDGs provide only “very tentative suggestions for review structures at the national, regional, and global level.” No mechanisms are provided for “independent civil society monitoring, data collection, and reporting.”4 Emerging research on the involvement of grassroots organisations (box 1) in social accountability interventions suggests that these organisations could bridge that gap.567 Data produced by users of services and collected by grassroots organisations may be useful for monitoring the SDGs. This information could be used to contrast with, and complement, the data collected by official sources. Box 1 ### What are grassroots organisations? Grassroots organisations are groups of people pursuing common interests, largely on a volunteer and not-for-profit basis. Often such organisations are formed by activists in social movements. Many are closely linked to communities and local concerns. The term often refers to voluntary associations through which “disadvantaged people organise themselves to improve the social, cultural, and economic wellbeing of their families, communities, and societies.”8 People have different conceptions of what constitutes the grassroots, but we apply the term to associations … RETURN TO TEXT
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".