Altmetrics Definitions and Use Cases
Bibliographic record
Abstract
The NISO Alternative Assessment Metrics Initiative was begun in July 2013 with funding from the Alfred P. Sloan Foundation, and divided into two phases. Phase II of the Project, which began in late 2014, set out to develop standards covering particular action items identified in Phase I through the creation of three NISO working groups. This document represents the output of the working group tasked with the following action items: 1. To come up with specific definitions for the terms commonly used in alternative assessment metrics, enabling different stakeholders to talk about the same thing; and 2. To identify the main use cases for altmetrics and the stakeholder groups to which they are most relevant, and to develop a statement about the role of alternative assessment metrics in research evaluation The following individuals served on NISO Altmetrics Initiative Working Group A, which developed and approved this Recommended Practice: Rachel Borchardt American University Library; Robin Chin Roemer University of Washington; Dianne Cmor Nanyang Technological University; Rodrigo Costas Centre for Science and Technology Studies, University of Leiden; Tracey DePellegrin Genetics Society of America; Sharon Dyas-Correia University of Toronto; Brigitte Jörg Thomson Reuters; Martha Kyrillidou Principal, Martha Kyrillidou & Associates; Jean Liu Altmetric; Joshua Lupkin Tulane University; Beth Martin University of North Carolina Charlotte, J. Murrey Atkins Library; Kim Mitchell SAGE Publications; Martin Fenner DataCite; Karen Gutzman Northwestern University Libraries; Michael Habib Independent, scholarly communications, publishing, library markets; Sharon Parkinson Emerald Publishing Group; Isabella Peters Leibniz Information Centre for Economics; Sheila Yeh University of Denver University Libraries
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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 | 0.045 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.028 | 0.047 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".