Reinforcing Reproducibility and Replicability: An Introduction
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
The purpose of scientific publishing is the dissemination of robust research findings, exposing them to the scrutiny of peers.The key to this endeavor is documenting the provenance of those findings.Scientific practices during the course of research and subsequent publication, peer review, and dissemination practices and tools, all interact to (hopefully) enable a meaningful discourse about the veracity of scientific claims.However, while all practices and tools contribute to the final output, some are less often discussed than others, and perceptions, usage, and acceptance differ in myriad ways across disciplines.In this special theme, and in a subsequent column called "Reinforcing Reproducibility and Replicability,"we will explore these topics, with expert providers and expert users providing their input.While we will start within the economics discipline in this special theme, the column will not be as narrowly focused, providing context and voice from other disciplines over time.Whether or not one actually believes there is a "replication crisis" (Fanelli, 2018), some doubts have been expressed in recent years about the reliability of research.Partially in response, there has been an increased emphasis on various methods that support improved provenance documentation.In the social sciences, this
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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