Current and Upcoming Challenges in South-South Cooperation in the field of Social Statistics
Bibliographic record
Abstract
This article proposes to reflect on how a field of technical expertise is affected and could possibly be transformed through South-South cooperation. This article and its conclusions are based on literature review and our own research. Our research on the production, the analysis and the dissemination of social development statistics (education, health and poverty alleviation) has been conducted in Western and North Africa as well as South East Asia and Central America. We interviewed public servants from government agencies, for international organisation, from non-governmental organizations as well as specialists from the academia. Our goal is to reflect on South-South cooperation as a possible tool for emancipation and criticism of the hegemony of “Northern” countries in international organization and knowledge production. By examining cooperative praxis in these areas we attempt to yield information on the strategies used to claim ownership of the assessment methods and knowledge pertaining to the ideological dimensions of development. We conclude by saying that current South-South cooperation in the field of social statistics is not fully able to challenge global statistics regime and its inherent ideological flaws.
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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 | 0.118 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".