The Social Construction of Christian Persecution Through Quantification in International Religious Freedom Advocacy
Bibliographic record
Abstract
Abstract A perception that Christianity is under attack has animated American political culture, shaping domestic politics that advance Christian political power as well as foreign policies aimed at protecting Christians worldwide. This paper examines how knowledge entrepreneurs within the international religious freedom advocacy field construct Christian persecution as a social problem through quantification efforts. Based on 18 months of ethnographic fieldwork in Washington, DC, I identify two discursive strategies through which knowledge entrepreneurs construct Christian persecution, specifically claiming that Christians are the most persecuted religious group worldwide. First, knowledge entrepreneurs misinterpret data by Open Doors on Christian persecution and the Pew Research Center on religious restriction through the process of omitting comparisons and conflating concepts. Second, knowledge entrepreneurs leverage the perceived objectivity of quantification to claim ideological neutrality. In a politically polarizing American context, these discursive strategies provide legitimacy to claims that Christians are the most persecuted religious group worldwide.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".