Persistent Identifiers as Open Research Infrastructure to Reduce Administrative Burden
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
Persistent Identifiers, or PIDs, are emerging as a key aspect of research infrastructure. They act as connective tissue, exposing the relationships between different entities that make up the research ecosystem. One of the major promises of PIDs is that they can help to reduce researcher administrative burden by automating the exchange of information that currently relies on manual entry. This benefit is not well understood by researchers, in part because it can only be realized when PIDs are adopted by a critical mass of researchers, funders, and research administrators. This article will outline the defining characteristics of identifiers, articulate the major benefits of research identifiers, discuss some of the main implementation challenges, provide an overview of existing and emerging identifiers, and summarize some key recommendations for expediting the adoption of PIDs around the globe.
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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.021 | 0.002 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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