Bibliographic record
Abstract
Effective open science is more than removing paywalls. Indeed, it requires that research outputs are Findable, Accessible, Interoperable, and Reusable (FAIR). The engine that powers the FAIR principles at scale is high-quality, open metadata. This poster illustrates how Crossref's infrastructure empowers members to curate rich metadata for their records, offering a gateway to FAIR data. We demonstrate how our metadata directly support each principle: Research is findable through persistent identifiers and rich descriptive metadata, It becomes accessible through standardized resolution to the content itself, Interoperability is ensured by machine-readable formats and links to related identifiers from partner registrars such as ORCID and ROR, and create a connected graph of knowledge, And, finally, reusability is enhanced by explicit license information that clarifies how both the metadata and the research it describes can be reused. For Canadian organizations building their open science systems, understanding, leveraging, and enriching this metadata is crucial. This poster offers a guide for researchers, administrators, and funders on how to use Crossref’s open tools to ensure their research outputs are not just open, but truly FAIR and impactful.
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 | 0.154 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.036 | 0.082 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.013 |
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".