Altmetrics as indicators of economic and social impact
Bibliographic record
Abstract
Chair: Euan Adie, Founder of Altmetric.com - Anup Kumar Das, Jawaharlal Nehru University – Altmetrics and the Changing Societal Needs of Research Communications at R&D Centres in an Emerging Country: A Case Study of India - Juan Pablo Alperin, Simon Fraser University – Evolving altmetrics to capture impact outside the academy - Lauren Ashby (SAGE) & Mathias Astell (Nature Publishing Group) – The Empty Chair at the Metrics Table: Discussing the absence of educational impact metrics, and a framework for their creation - Prof Theng Yin Leng, Nanyang Technological University, Singapore – Altmetrics: Rethinking and Exploring New Ways of Measuring Research Outputs - Rodrigo Costas, (CWTS-Leiden University, the Netherlands) & Stefanie Haustein (Université de Montréal, Canada) – Citation theories and their application to altmetrics
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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; both teacher heads agree on what is shown here.
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".