Lebanon and Iraq: Two Distinct Demonstrations of Confessionalism’s Failure as a Means to Manage Ethnic and Religious Pluralism
Bibliographic record
Abstract
The Iraqi and Lebanese political systems stemmed from each country’s distinctive mosaic of sub-national identities but have been deemed corrupt and incompetent, prompting ongoing protests and calls for unity in both contexts. However, this dissatisfaction is unsurprising given the challenging task of translating the ethnic, linguistic, and religious diversity that characterizes each population into an overarching national identity. The Lebanese and Iraqi political systems have attempted to manage ethnic and religious pluralism through confessionalism, or a “consociational government which distributes political and institutional power proportionally among religious sub-communities.” This paper argues that Lebanon and Iraq are two specific examples of confessionalism, demonstrating its failure to manage ethnic and religious pluralism, which seems to inevitably beget sectarianism—a discriminatory structure in which each group advances its privileges at the expense of others. Nevertheless, confessional systems are challenging to transform, namely because they institutionalize different ethnic or religious groups’ identities instead of promoting a unified, national identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".