Registered Reports: Genre Evolution and the Research Article
Bibliographic record
Abstract
The research article is a staple genre in the economy of scientific research, and although research articles have received considerable treatment in genre scholarship, little attention has been given to the important development of Registered Reports. Registered Reports are an emerging, hybrid genre that proceeds through a two-stage model of peer review. This article charts the emergence of Registered Reports and explores how this new form intervenes in the evolution of the research article genre by replacing the central topoi of novelty with methodological rigor. Specifically, I investigate this discursive and publishing phenomenon by describing current conversations about challenges in replicating research studies, the rhetorical exigence those conversations create, and how Registered Reports respond to this exigence. Then, to better understand this emerging form, I present an empirical study of the genre itself by closely examining four articles published under the Registered Report model from the journal Royal Society Open Science and then investigating the genre hybridity by examining 32 protocols (Stage 1 Registered Reports) and 77 completed (Stage 2 Registered Reports) from a range of journals in the life and psychological sciences. Findings from this study suggest Registered Reports mark a notable intervention in the research article genre for life and psychological sciences, centering the reporting of science in serious methodological debates.
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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 | 0.105 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".