Civil Society Leaders’ Perceptions of Hopes and Fears for the Future
Bibliographic record
Abstract
Civil society actors such as nongovernmental voluntary community group leaders as well as funding agency development officers have taken a leading role in implementing grassroots-level peacebuilding efforts in post peace accord Northern Ireland. It is important to map these civil society leaders’ direct experience and their perceptions to assess the impact of the funding in the overall peace process in Northern Ireland. This article captures the hopes and fears of 120 civil society leaders and funding agency development officers in Northern Ireland and the Border Counties whose projects are funded by the International Fund for Ireland and/or the European Union Peace III Fund. Many respondents concurred that both funds have genuinely contributed toward achieving the overall goals of peacebuilding (though mostly at the grassroots level) while some were skeptical about the future sustainability of the peace process. Their skepticism was evident, as few tangible changes at the macro political–economic level have occurred since 1998 as the funding draws to an end in 2013. However, the respondents also highlighted the uncertain future that now exists for the younger generation. There is also an absence of effective leadership in the wake of the recent trend of dissident violence, which has a denegrating effect on envisioning a peaceful and just society for all citizens in Northern Ireland.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".