Persistent Identifiers as Open Research Infrastructure to Reduce Administrative Burden
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.
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 | 0.205 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.026 | 0.071 |
| Open science | 0.009 | 0.050 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".