Exploring the influence of religion on the politics of addressing forced child begging in Senegal
Bibliographic record
Abstract
Scholars and democracy watchdogs have praised Senegal for its relatively robust democracy, history of peaceful transitions of power, and peaceful relations between the Muslim majority and Catholic minority, among other traits. Moreover, commentators have occasionally attributed this success to the values espoused by members of the Sufi majority and their interpretation of Islam. This praise stands in sharp contrast with the reality of Senegal’s human rights record. Prominent human rights NGOs such as Human Rights Watch and Amnesty International have drawn attention, in particular, to the practice of child begging, or forced child begging (FCB), which emerges out of Senegalese Koranic schools, better known as daaras. This thesis will explore efforts to address the human rights violations associated with FCB in Senegal. In particular, by analyzing political discourse surrounding the issue of FCB and efforts to address it, will probe the relationship between the promotion of human rights norms which would entail the eradication of FCB, and the influence of religion on Senegalese politics. In this way, it demonstrates the limits of IR theories on human rights norms, and helps explain how a country that is often applauded for its human rights record still struggles to address this critical human rights issue
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".