Sorry cites: The (necro) politics of citation in the anthropology of religion
Bibliographic record
Abstract
In this article, I analyze the under-citation of Black and/or Latine scholars—especially those located disciplinarily within religious studies—in the anthropology of religion. I draw from my own experience as an editorial assistant at History of Religions, manuscript reviewer, and Latine ethnographer of religion to speculate on the reasons why researchers might refuse to cite them, preferring either to neglect their contributions or to “plagnore” them, to borrow a term coined by legal scholar, law professor, and activist Lolita Buckner Inniss. I then expand on Chicana and Boricua feminist and race scholar Nichole Margarita Garcia’s theorization of under-citation as “spirit-murdering.” I invoke philosopher and political scientist Achille Mbembe’s formulation of necropolitics to make the case that citation is a matter of life and death for Black and Latine women scholars in particular. In the absence of institutional accountability for editors and authors, I conclude with recommendations for the diversification of our scholarship and syllabi.
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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 | 0.026 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".