Bibliographic record
Abstract
This article explores the relationship between the Church of Scientology and various forms of media, in particular the Internet. Building on insights in the academic literature, this piece attempts to fill a lacuna by giving more attention to some of Scientology’s own media programs and efforts. With these in mind, the Church of Scientology is a case study in the challenges that a new religion faces in legitimating itself to an increasingly globalized audience in the digital age. On a popular level, Scientology parishioners seem increasingly open to discussing, defending, and disseminating Scientology on social media platforms. These efforts may encourage others accustomed to a Scientological theology of evil in which “entheta” should be avoided and “suppressive persons” (SPs) shunned. As such, socially engaged Scientologists, in particular second- and third-generation members, may become witting and unwitting foot soldiers on behalf of the church in waging an ongoing public relations war, and thus poised to legitimate Scientology to outsiders disinterested in or suspicious of “institutional religion.” This hypothesis is all the more intriguing and plausible in the American context, given the market share created by the heterogeneous “rise of the nones” (religiously unaffiliated/disaffiliated populations).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.008 |
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".