Calm and Humble In and Through Evangelical Christianity: A Chinese Immigrant Couple in Toronto
Bibliographic record
Abstract
Researchers have observed that immigration often increases religiosity among the religious and leads to conversion among the non-religious (Li, 2000; Yang, 1999), that ethnolinguistic immigrant groups tend to become more religious in their adopted countries and embrace more conservative religions than the mainstream, and that old and new religions are booming among these minority groups (Carnes and Yang, 2004; Yang and Ebaugh, 2001). Academics and journalists have noticed that evangelical Christianity is gaining popularity in the developed countries (Bramadat, 2000, 2005; Jule, 2005), but particularly among immigrants in the developed and among the mass in the developing world (Bergner, 2006; Hallum, 1996; Wakin, 2004). Some researchers find that Korean American males compensate for their loss of status by gaining status in church (Kurien, 2004), while issues of language (Woods, 2004), race (Kim, 2004; Park, 2004), gender and generation (Yang, 2004) are found to either unite or divide some minority churches in Australia and the United States. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".