Bibliographic record
Abstract
This chapter offers a comparative look at the ways in which the international community prioritized peacebuilding agendas of intervention linked to national and local spaces. In other words, it examines how each international actor foregrounded agendas in targeted spaces for peacebuilding intervention, using examples from online communications to illustrate some of the broader trends observed. Agendas were linked to recommendations for action, information about activities or strategies carried out, and expressions of support, concern or condemnation in online subsidies. The latter was included in the analysis as it reflected the desire for something to be addressed (agenda setting) and a call for state action (see Appendix 1 for a description of agendas). Overall trends Figure 7.1 shows the overall number of subsidies found per agenda. Most communications referred to development, followed by peace process, security, governance and HR agendas. Regarding development, in particular, most subsidies referred to social fabric (159), followed by economic development and food security (141), environment (65) and media and communication (13). In relation to the peace process, specifically, most subsidies referred to disarmament, demobilization and reintegration (DDR) and recruitment prevention (122), followed by negotiation and implementation of the peace agreement (119) and transitional justice (TJ) (66). Concerning security, most subsidies included comments on monopoly of force (113), followed by tackling illegal economies (55) and de-mining (21). In terms of governance, most online subsidies referred to institutionalization and the rule of law (108), followed by infrastructure and social services (62) and land governance (46). Regarding human rights, most communications mentioned HR alongside humanitarian aid (89), followed by security guarantees for HR defenders (62) and against gender-based violence (22). Figure 7.2 compares actors using percentages of subsidies linked to different agendas per total of subsidies for each actor's total subsidies. In other words, it shows which actors focused the greatest percentage of their online subsidies to comments in support of each agenda, in comparison with other actors. In general, the US, EU, IDB and World Bank foregrounded socio-economic development; Canada and the UK, security agendas; Sweden, the UN and OAS, aspects related to the peace process. Comments around human rights were not identified as a theme in either the World Bank or IDB subsidies, and the latter organization did not refer directly either to the peace process or security dynamics.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| 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".