Bibliographic record
Abstract
The text asks five questions regarding reverse mission. First, what is it? Secondly, is it actually being attempted? Thirdly, why “diasporic” attempts lag behind the rhetoric? Fourthly, why do “non-diasporic” attempts, on the other hand, have more chance of success? And lastly, what is the outlook for reverse mission in Europe? The concept of reverse mission is defined and it’s imprecision questioned. Attempts at diasporic reverse mission are then discussed, as well as reasons for their general failure. However, non-diasporic reverse mission, in various modalities, is also widespread. The advantages and disadvantages of this direct mission movement are analyzed, stressing the specific disadvantages that Pentecostals face in Europe. By far the most significant cross-cultural success story by global southern missionaries in Europe is in Ukraine, but there are many reasons for regarding Ukraine as exceptional rather than a foretaste of things to come. The article concludes that reverse mission via diaspora churches (to which disproportionate scholarly attention has been paid) is unlikely to work. Non-diasporic reverse mission (which deserves more research) is more promising, but even so faces huge obstacles and positive results are still few and far between (except in Ukraine). Over time, however, some success may be achieved; possible scenarios for such success in Europe are discussed.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".