Analyzing a Pentecostal "revolution": Reflections on research methodology
Bibliographic record
Abstract
This study evaluates a research questionnaire and responses in a recent study on the influence of the Pentecostal movement in Nigeria. It observes areas of potential confusion within the questionnaire due to its wording, and suggests revisions and additional questions that might improve the relevance of the questionnaire. Through further analysis, it notes questions that may be unintentionally written with bias, thereby influencing responses, and discusses what are suggested to be the advantages and disadvantages of formatting questions in a specific manner. Finally, this study explains the process of using the aforementioned questionnaire to create an electronic database in which to organize responses. It evaluates the use of the database as a research tool and comments on the place of quantitative analysis as a whole within the study of history. Analysis of the questionnaire and database concludes that both were largely successful in accomplishing the goals of the study for which they were created. The database is found to be a valuable tool as it provides an efficient way to establish trends and focus further analysis of the information collected. The importance of this tool to the research project suggests that quantitative analysis can be of great use in historical research, though it should not replace traditional research methodology in this field. Keywords: mixed research methodologies (evaluation of); survey research; religion and politics; Pentecostal revival; Nigeria
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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 | 0.252 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".