Programmatically Ranking and Sorting Research Articles for Reviews (ALIGN)
Bibliographic record
Abstract
In an emerging trend to automate the world and daily interactions, academia is not exempt. Systematic reviews have been used in clinical research for decades and in practice, the process involves at least a double-blind sorting of articles in order to reach conclusions on a specific topic. With over 13 different values, a researcher will seldom need to consult other external resources to assess the quality of the paper. As such, ALIGN provides a nearly self-contained research application, which can be used to simplify and streamline the process of writing systematic reviews, while ensuring accuracy and quality. In general, by adding about 40-60 seconds of computing time per paper, researchers can begin to access objective measurements of the paper. This paper explores the different factors that go into evaluating a paper in general, the amalgamation of different resources to summarize useful information about the papers found in the search strategy, as well as the implications and limitations of the process on academia.
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.
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 | 0.083 | 0.343 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".