Bibliographic record
Abstract
his special issue has its genesis in an offhand query of a sort that the reader has almost surely received: to whit, a neighbor asked me over drinks if Japanese people are religious.For years I have answered these perennials with a variety of yes-and-no responses, and to be sure, there is ambivalence regarding the contribution of religions and religious ideas to modern and contemporary Japanese society, culture, and politics.On the one hand, one finds public opinion polls in which the majority of Japanese deny having religious sentiments, as in a recent (2019) International Social Survey Programme (issp) poll on "How Have Japanese Attitudes and Behavior on Religion Changed?" Respondents concluded that:(1) While the percentages of believers of different religions remain almost the same, religious faith is weakening, and people pray to the gods less frequently;(2) There is a decrease in those who feel that "the sun (God) is watching them, " that "supernatural powers exists [sic]," and that "deities dwell in nature"notions that have long been regarded as traditional religious values, beliefs, and practices of the Japanese; and (3) those who expect religions to play certain roles such as "providing comfort" are decreasing, while those who find religions dangerous outnumber those who do not.(www.nhk.or.jp/bunken/english/research/yoron/20190401_7.html)In a similar vein, contemporary post-Aum (post-1995) Japanese society regularly comes across as hostile toward unorthodox religious expression, and at
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".