Majoritarian Epistemology on Religious Symbols. A Religiously-Based Stereotyping Technique to “Package Others’ Religious Rights”
Bibliographic record
Abstract
The paper will focus on a particular form of stereotyping technique which aims to narrow religious rights for non-Christian believers, moving from an exclusively Judeo-Christian epistemology on religious symbols that, no by chance, defines them as “ostensive”. According to this perspective, freedom of religion is eminently a heartfelt attitude, therefore the term “ostensiveness” is intended to emphasize not mandatory behaviors, which are conceived as a redundant way to live faith. Starting from its philosophical assumptions, the article deals with the stereotyping tools related to religion, functional to conceal the social complexity and to deny legal protection, through a legal and political concept like state neutrality. The piece seeks to show how the concept of religious right, when it cannot be declined as a majoritarian right, is rife with plural levels of intersecting stereotyping, concerning other categories of diversity like gender and ‘ethnicity’. This approach flatters each dimension and does not take into account coexisting identities within the same person, ignoring that intersectionality highlights the necessity of assessing religious diversity as fundamentally socially located. This stereotyping attitude can be traced back to the complex relationship between law and religion that provides a direct way to assess crucial issues like belonging, identity, community and authority. Law, as a cultural and non-neutral construct, regards religion as a valuable fact and worthy of legal protection since it is attributable to an individual phenomenon and as quintessentially private matter. Therefore, to assess identity or belonging in the fault lines of the interaction of law and religion means find an opportunity to legitimize targeting law related to religious diversity making it seems like a way to deal with religious ‘differences’ that cannot be assimilated. In this respect, we discuss about the radical secularist claims through a case-study, namely the “affaire Québécois” within the Canadian system, not only in a geographical sense, but in the theoretical field mapped out by religious pluralism as the focal point of the multiculturalist approach, on one hand, and the secularist revival, on the other hand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".