Towards Inclusive Research Assessment: Recognizing Research Artefacts Beyond Publications
Bibliographic record
Abstract
This document is an output of the ELIXIR STEERS project and synergistic to its Policy Brief on Strategies for Enhancing Credit and Recognition for Research Artefacts. It is planned to be a living document that will be versioned and improved based on community input. It will be hosted on Zenodo and versioned accordingly. It is synergistic to initiatives supporting research artefact credit and recognition reform such as ELIXIR STEERS, EOSC EVERSE, DORA and CoARA. It aims to support wider research activity and artefact recognition reform by providing: Research Activities & Artefacts Table Controlled Vocabulary for Research Activities & Artefacts
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 | 0.069 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.055 | 0.057 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".