From stakeholders to protagonists: an exploratory framework for cultivating prosocial capacities for development
Bibliographic record
Abstract
The world in 2024 faces numerous interlinked crises, including climate change and water shortages, rising geopolitical tensions, and a new awareness of the risks of pandemics. These crises reverse decades of incremental development progress and humanity’s aspirations embodied in the 2030 Agenda for Sustainable Development, necessitating a more active and collaborative participation of development stakeholders. The magnitude of challenges points to the need for transformational approaches to releasing the potential of stakeholders, which requires building on and extending beyond current best practices in participation and capacity strengthening. What is most needed today is a balanced assessment of the complexity of human nature and a vision that recognizes the prosocial potential of people to harmonize the pursuit of personal interests with a willingness to contribute to social and collective development goals. Prosociality is a capacity that all stakeholders can strengthen—from individuals to institutions to communities (including different forms of social groupings). These stakeholders can become empowered as active protagonists of development, with the potential to work synergistically to achieve the Sustainable Development Goals (SDGs). In order to work with these protagonists, it is important to view them systematically in terms of key characteristics such as their antecedent knowledge, values and culture, stance, agency, roles, relationships, and learning.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.068 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".