Authorship, Patents, Citations, Acknowledgments, Tweets, Reader Counts and the Multifaceted Reward System of Science
Bibliographic record
Abstract
ABSTRACT Building upon well‐established paradigms brought forth by such theorists as Robert K. Merton, Pierre Bourdieu, and Blaise Cronin, the panel will span the full cycle of academic production to show, through various bibliometric measures and other quantitative and qualitative analyses, how the reward system of science is evolving. While there is strong evidence to suggest that such forms of dissemination as social media output and blogging are being incorporated into scientific practices, scientific impact still remains principally assessed using measures such as authorship and citations, whilst other elements, such as acknowledgements, have received varying levels of regard at various times. Disciplinary considerations also arise. Using a wide range of approaches, measures, and datasets, the panelists will establish links between their individual research to create an empirically driven picture of the reward system of science and its indicators. Through the use of the Polldaddy application, audience members will answer questions and create an overview of their perception of the reward system of science.
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 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 | 0.014 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".