Introducing Registered Reports at <i>Language Learning</i>: Promoting Transparency, Replication, and a Synthetic Ethic in the Language Sciences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The past few years have seen growing interest in open science practices, which include initiatives to increase transparency in research methods, data collection, and analysis; enhance accessibility to data and materials; and improve the dissemination of findings to broader audiences. Language Learning is enhancing its participation in the open science movement by launching Registered Reports as an article category as of January 1, 2018. Registered Reports allow authors to submit the conceptual justifications and the full method and analysis protocol of their study to peer review prior to data collection. High‐quality submissions then receive provisional, in‐principle acceptance. Provided that data collection, analyses, and reporting follow the proposed and accepted methodology and analysis protocols, the article is subsequently publishable whatever the findings. We outline key concerns leading to the development of Registered Reports, describe its core features, and discuss some of its benefits and weaknesses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.145 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it