Metrics Literacy: Educating Researchers and Research Support Staff Regarding Scholarly Metrics
Bibliographic record
Abstract
The talk introduces the Metrics Literacy project, which aims improve the way in which researchers and research support staff (e.g., research managers, research librarians, science communicators and funding organizations) can be trained to ensure that scholarly metrics are applied and interpreted appropriately. It aims to reduce misuse of indicators, such as the impact factor and h-index, and the application of quantitative measurements in inapt contexts by developing online resources (so-called building blocks), which convey the meaning of various scholarly indicators in an easy-to-understand fashion. Building blocks are targeted at specific audiences (e.g., researchers or funders) and address one of five main questions and four fields of application (i.e., usage metrics, altmetrics, bibliometrics and technometrics) and will be distributed using the Software Carpentry framework. The project intends to improve metrics literacy in academia and inform current scientometric research about the use of scholarly metrics.
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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 | 0.075 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".