Language Appropriation and Identity Construction in New Religious Movements: Peoples Temple as Test Case
Bibliographic record
Abstract
This article uses sociolinguistic research on cultural markers, combined with Tim Murphy’s semiotic theory of religion, to argue that linguistic fluency signals and shapes group identity in new religious movements. Asserting that religions are systems of signification with shifting meanings, I argue that examining acts of language appropriation lets scholars explain the influences, concerns, and behaviors of new religions. Moreover, I use Murphy’s focus on asymmetrical relations to show that new religions appropriate and recode extant terms in ways that disempower competing groups while simultaneously constructing their own identity. To demonstrate this theory, I examine language appropriation in Peoples Temple; specifically, Jim Jones’s recoding of the racial slur nigger. Jones simultaneously supported and subverted nigger’s usual connotations to critique American society while casting his congregation as a persecuted—but ultimately noble—minority. This recoding encouraged members to express unity by accepting collective guilt, contributing to the group’s demise.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".