How can traditional statistical relationships be redefined through citizen to government partnerships?
Bibliographic record
Abstract
The post-2015 Development Agenda proposes to produce much more statistics and data than currently available in the official arena through advanced methods and innovative partnerships. By associating governments and data producers of all kinds it aims to monitor the Sustainable Development Goals (SDGs). The objective of this paper is to explore and analyse one of the 2030 Agenda greatest challenges, i.e. to redefine traditional statistical relationships and processes to associate citizenry as an active stakeholder in the monitoring of SDGs. It proposes innovative ideas linking citizen-to-government and government-to-citizen data partnerships (C2G dp and G2C dp) to the SDG requirements. The paper portrays and analyses the benefits for parties of alternative projects from Uganda, Canada and Uruguay. The C2G dp Stats Up program is featured as an additional case study, describing its achievements and shortcomings. This contribution constitutes a valuable co-creation case to fill the gap of lack of partnering skills. In sum, the paper presents the added value of a constructive socio-technical approach to SDG 17. Final conclusions propose a roadmap to support the work of National Statistical Offices to address complex challenges to walk the talk of the 2030 Agenda harnessing the crucial role of civil society in their plans.
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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 | 0.197 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.038 | 0.054 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".