Building Transnational Capacity for Policy Advocacy on Internet Freedom: A Discussion Paper
Bibliographic record
Abstract
How do “Internet freedom” advocates in the Global South define ‘effective’ policy advocacy? What types of challenges do these advocates face that diminish their efforts and effectiveness at national and global governance levels? Finally, what are the benefits and drawbacks of donor-driven programs using intermediary organizations to address capacity-building challenges? This paper presents findings from early-stage case study research on these questions, featuring a primarily donor-driven transnational capacity building initiative launched in 2012 called the “Internet Freedom and Human Rights (IFHR)” program. Coordinated by Global Partners & Associates based in London, with assistance from a team at the New America Foundation’s Open Technology Institute (OTI) based in Washington, DC, the IFHR program seeks to strengthen NGO capacity, particularly in the Global South, for “effective” policy advocacy on Internet Freedom issues at both national and international levels. The IFHR program enjoys support from the Ford Foundation, the Open Society Foundation, the Media Democracy Fund, and the U.S. State Department. The paper features a preliminary literature review of the relevant capacity building literature. It also highlights opportunities and challenges experienced thus far in seeking to expand policy expertise and strategic advocacy that enhance civil society interventions in global discussions about current and future approaches to Internet regulation.
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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 | 0.034 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".