Bibliographic record
Abstract
Beginning in the early 1970’s with the invention of the microprocessor, mass use of information technologies worldwide coincided with the appearance of a nodally-linked network of digital interconnectivity, or ‘network society’ (Castells, 1996). The network society’s exponential growth correlates with a rise in use of digital networking media by various sects and denominations of the Christian religion. Today, growing numbers of Christian organizations integrate digital media into both their approach to worship and the dissemination of the Holy Scriptures. This paper argues that the use of digital media by these organizations is indicative of the creation of a “religious network society†exhibiting identical structural paradigms to Castells’ (1996) network society. By virtue of the media deployed within it, the ‘religious network society’ fosters a mass culture of digital participation characterized by a rapid fragmentation of religious messaging and an over-sharing of personal religious beliefs. However, the religious network society also erodes Christianity’s hierarchical structures of authority (Turner, 2007). It is argued that these structures are being replaced with a banal form of religion emphasizing spirituality and individual self-expression at the expense of tradition (Campbell, 2012; Hjarvard, 2013). Moreover, purpose alterations to Christianity’s authority structures and approach to worship are indicative of a much larger shift in the religion, in which rising digital media use may in fact imply a decline in Christianity’s societal influence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".