Data for: How much research shared on Facebook is hidden from public view?
Bibliographic record
Abstract
All data required to reproduce results of "How much research shared on Facebook is hidden from public view?". More information about the manuscript, code, and reproducibility can be found here. This dataset contains five spreadsheets from two different sources: 1. Data collected with our own method described in Enkhbayar and Alperin (2018). More details and instructions can be found in this GitHub repository. plos_one_articles.csv: All articles published in PLOS ONE from 2015 - 2017 altmetric_counts.csv: POS and TW counts retrieved from Altmetric™ graph_api_counts.csv: AES counts collected with our methods using Facebook's Graph API query_details.csv: Responses from Graph API 2. Data provided by Piwowar et al. (2017) PLOS_2015-2017_idArt-DOI-PY-Journal-Title-LargerDiscipline-Discipline-Specialty.csv: Disciplinary categorisations for PLOS ONE publications as described in Piwowar et al. (2015) References Enkhbayar, A., & Alperin, J. P. (2018). Challenges of capturing engagement on Facebook for Altmetrics. STI 2018 Conference Proceedings, 1460–1469. Retrieved from http://arxiv.org/abs/1809.01194 Piwowar, H., Priem, J., Larivière, V., Alperin, J. P., Matthias, L., Norlander, B., … Haustein, S. (2018). The state of OA: A large-scale analysis of the prevalence and impact of Open Access articles. PeerJ, 6, e4375. doi: 10/ckh5
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 | 0.003 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.446 | 0.335 |
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".