Altmetrics Data Quality Code of Conduct
Bibliographic record
Abstract
Altmetrics are increasingly being used and discussed as an expansion of the tools available for measuring the scholarly impact of research in the knowledge environment. The NISO Alternative Assessment Metrics Project was begun in July 2013 with funding from the Alfred P. Sloan Foundation to address several areas of limitations and gaps that hinder the broader adoption of altmetrics. This document is one output from this project, intended to help organizations that wish to use altmetrics to ensure their consistent application across the community. “Working Group C” studied and discussed issues of data quality in the altmetrics realm, an essential aspect of evaluation before metrics can be used for research and practical purposes. Additional working group outputs from this initiative in the areas of definitions, use cases, specific output types and use of persistent identifiers will be released soon for public comment. The Code of Conduct aims to improve the quality of altmetric data by increasing the transparency of data provision and aggregation as well as ensuring replicability and accuracy of online events used to generate altmetrics. It is not concerned with the meaning, validity, or interpretation of indicators derived from that data. Altmetrics are based on online events “derived from activity and engagement between diverse stakeholders and scholarly outputs in the research ecosystem,” as defined in the forthcoming NISO Recommended Practice, Altmetrics Definitions and Use Cases (NISO-RP-25-201X-1). The following individuals served on the NISO Altmetrics Working Group C, which developed and approved this Recommended Practice: Euan Adie Altmetric; Scott Chamberlain rOpenSci; Tilla Edmunds Thomson Reuters; Martin Fenner DataCite; Gregg Gordon Social Science Research Network (SSRN); Stefanie Haustein (co-chair) Université de Montréal; Kornelia JungeJohn Wiley & Sons, Ltd.; Stuart Maxwell Scholarly iQ; Angelia Ormiston Johns Hopkins University Press; Maria Stanton American Theological Library Association (ATLA); Greg Tananbaum (co-chair) Scholarly Publishing and Academic Resources Coalition (SPARC); Joe Wass Crossref; Zhiwu Xie Virginia Tech University Libraries; Zohreh Zahedi Centre for Science and Technology Studies, University of Leiden
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchScholarly communication Domain: Reproducibility · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.203 | 0.583 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.094 | 0.079 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".