Bibliographic record
Abstract
New technologies are changing how and when we learn about events and choose to respond to them. Mobile phones and the internet have altered how we engage with the world. With technology usage expanding rapidly in the developing world, new avenues of participation, engagement, and accountability are emerging. Globally, more people now have the opportunity to actively make use of these tools to participate in processes that impact their societies. This opportunity for participation is also an opportunity for engaging in new ways with peacebuilding processes. As the field of technology for peacebuilding grows, most attention has been paid to the potential of new technologies for bridging the gap between warning and response. Whilst the focus on the use of technology for early warning and response is important, there is more to this growing field. The empowerment of people to participate in localized conflict management efforts is one of the most significant innovations and opportunities created by new technologies. Technology can contribute to peacebuilding processes by offering tools that foster collaboration, transform attitudes, and give a stronger voice to communities. This article aims to give practitioners two related frameworks to understand how new technologies can enhance peacebuilding. The first section looks at the functions that technology can have in a peacebuilding program as a tool for data processing, communication, engagement, and gaming. We then examine the program areas that new technologies can best contribute to, covering early warning/early response systems, programs that allow citizens to voice their opinions and experiences, collaboration efforts, and programs aimed at transforming attitudes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.174 | 0.058 |
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".