Narrative reanalysis: A methodological framework for a new brand of reviews
Bibliographic record
Abstract
In response to the evolving needs of knowledge synthesis, this manuscript introduces the concept of narrative reanalysis, a method that refines data from initial reviews, such as systematic and reviews, to focus on specific sub-phenomena. Unlike traditional narrative reviews, which lack the methodological rigor of systematic reviews and are broader in scope, our methodological framework for narrative reanalysis applies a structured, systematic framework to the interpretation of existing data. This approach enables a focused investigation of nuanced topics within a broader dataset, enhancing understanding and generating new insights. We detail a five-stage methodological framework that guides the narrative reanalysis process: (1) retrieval of an initial review, (2) identification and justification of a sub-phenomenon, (3) expanded search, selection, and extraction of data, (4) reanalyzing the sub-phenomenon, and (5) writing the report. The proposed framework aims to standardize narrative reanalysis, advocating for its use in academic and research settings to foster more rigorous and insightful literature reviews. This approach bridges the methodological gap between narrative and systematic reviews, offering a valuable tool for researchers to explore detailed aspects of broader topics without the extensive resources required for systematic reviews.
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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 | 0.663 | 0.756 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.013 | 0.018 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".