The significance of Bernard Lonergan's work on bias for the ethnic and religious conflicts in Sub-Saharan Africa
Bibliographic record
Abstract
Africa has often been perceived as a confluence of tension and conflict and the recent upheavals in Sub-Saharan Africa have done little to help this perception. The rising wave of ethnic and religious violence continues to drain the continent of its material and human resources, leading to what Bernard Lonergan describes as a state of "cumulative decline." Muslim-Christian relations in some African countries are currently at their lowest ebb. The world still remembers the Hutu-Tutsi conflict that led to savage massacres in Rwanda and the warlord politics of Somalia of the 1990s. Followers of the three main religions in Africa: African Traditional Religion (ATR), Islam and Christianity, are perceived to be at odds with one another and sometimes take extreme measures in their quest to seek relevance and assert themselves as a dominant religious force. The official introduction of Sharia law (Islamic legal code) in countries like Nigeria and Sudan is perceived to be an instance of the prevailing religious prejudice that has further compounded the problem, giving a religious twist to an already ethnically polarized people. The Sharia law has been met with stiff resistance from Christian groups who consider it a breach of their fundamental human rights and right to religious liberty. The resistance has led to an upsurge in religious violence, further exacerbating the carnage. Scholars have, at various times, examined the cause of ethnic/religious conflicts in Africa but have not been able to bring about a lasting solution. African bishops, theologians, and policy makers are still laboring to find a meaningful and lasting solution to the crisis. The Canadian Jesuit theologian, Bernard Lonergan (1904-1984) offers an analysis of bias that addresses the root cause of conflict in the human person and society, an analysis that can contribute to a deeper understanding of ethnic and religious conflict in Africa. He also offers resources for overcoming them. His work addresses the "oversights," "unreasonableness," and "irresponsibility" inherent in human conflicts that lead to a cumulative decline in both the human person and society. The import of this work lies in the fact that it brings into the African discussion the work of the renowned Jesuit theologian, Bernard Lonergan whose work on "bias" is significant for promoting a "responsible self," which in turn ensures the promotion of the common good and brings about a meaningful social change.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| 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".