Make Data Count Summit presentations
Bibliographic record
Abstract
The Make Data Count Summit took place in Washington DC on 12-13 September 2023 as a forum for dedicated discussion on data metrics and the evaluation of data usage. The event brought together representatives across research and research-supporting organizations, government and policy institutions, and infrastructure providers to discuss immediate needs to drive broader development and adoption of data metrics. We include the slides for the talks presented at the event: Welcome by Iratxe Puebla, DataCite ‘Defining the Need for Open Data Metrics’ by Daniella Lowenberg, University of California Office of the President ‘Forging the path forward: Global Data Citation Corpus’ by Matt Buys, DataCite and Carly Strasser, Chan Zuckerberg Initiative ‘Democratizing Data’ by Julia Lane, NYU Wagner Graduate School of Public Service ‘Five years since the US Evidence-act’ by Nancy Potok, NAPx Consulting and New York University ‘Meaningful Data Metrics: The State of Evidence from Bibliometrics Studies on Data Reuse’ by Stefanie Haustein, University of Ottawa and ScholCommLab, Nicolas Robinson-Garcia, University of Granada, Mike Thelwall, University of Sheffield, and Thed van Leeuwen, Centre for Science and Technology Studies, Leiden University
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.150 | 0.065 |
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".