Mapping Open Science Scholarly Literature
Bibliographic record
Abstract
Scholarly literature on open science over the past several decades has paralleled developments in research policy and practice, proliferated alongside mandates and directives, and increased in volume. Navigating the conceptually wide-ranging and versatile topic of open science makes analyzing its body of literature an ongoing challenge, often approached with a range of methods and perspectives. We use co-citations and direct citations to map the scholarly literature on open science and identify eleven clusters: open data, psychology-replication, tech and industry, participatory research, scholarly communication, neuroscience-reproducibility, social justice and diversity, public health-COVID-19, bio-data, publication bias/meta-research, and eating disorder-COVID-19, using Louvain community detection. This survey of the literature would prove useful for those looking to calibrate their research efforts with a dynamic and multifaceted area of inquiry, better navigate the field to understand its topical landscape, and perhaps influence or chart a course for the trajectory of scientific discourse related to open 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 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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.131 | 0.133 |
| Open science | 0.011 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".