Bibliographic record
Abstract
The ASI Sprint Report Series is dedicated to critical app studies enquiry, exploring the phenomenon of ‘appification’ and its diverse societal, cultural, and political-economic impacts globally. Published by the App Studies Initiative (ASI), the series showcases ongoing research conducted by ASI-affiliated researchers in collaboration with Master's students. Each report features the latest findings from recent ‘sprints,’ aiming to promptly disseminate ongoing research to the broader app and platform studies community. DOI: https://doi.org/10.17605/osf.io/hv34x. Series URL: https://appstudies.org/research-output/publications/asi-sprint-report-series/. The App Studies Initiative (ASI) is an international research network comprising academic experts in app-related media research who contribute to the study of apps and platforms. The research network involves researchers and faculty from the University of Amsterdam and Utrecht University (the Netherlands), the University of Warwick and Goldsmiths, University of London (United Kingdom), Concordia University and the University of Toronto (Canada), amongst others. Its directors are Anne Helmond, David Nieborg, Fernando van der Vlist, and Esther Weltevrede. Contact: @appstudies; https://appstudies.org/.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.424 | 0.444 |
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".