‘The Opportunity Exists. Why Don’t They Seize It?’ Political (In)Competence and the Potential of ICTs for Good Governance in Niger Republic
Bibliographic record
Abstract
An increasing number of scholars, political activists, humanitarian workers, and peacebuilding strategists are now advocating ICTs for fostering democratic participation and good governance in Africa. For their part, governments are devising policies geared towards helping citizens controlling their own destiny through the use of ICTs. They are backed by international development organizations that are implementing numerous programmes and projects centered on the notions of e-government, e-governance, and e-democracy. All those concerned actors and development workers are particularly encouraged by the fact that digital devices are becoming increasingly available for public use on the continent. However, so far, we lack clear evidence that African citizens are actually using, in their everyday lives, digital tools for governance, political participation, and peacebuilding purposes. Based on the case of Niger Republic, this study seeks to contribute to answering this question through semi-structured interviews carried out with Nigerien social media users. It shows that many factors including illiteracy, and particularly digital illiteracy, lack of political will, inefficient methods and poor understanding of social media potential contribute to strongly mitigate digital activities when it comes to access, governance, political participation and peacebuilding.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".