“It’s messy and it’s massive”: How has the open science debate developed in the post-COVID era?
Bibliographic record
Abstract
The COVID-19 pandemic accelerated the global adoption of open science (OS) practices. However, as the pandemic subsides, the debate around OS continues to evolve. This study investigates how the pandemic has shaped the OS discourse and identifies key issues and challenges. Interviews were conducted with influential actors across the research and publishing communities. The findings show that while many areas of debate remained constant, the ways in which they were discussed exposed underlying systemic challenges, which must be addressed if OS is to progress. These issues included the scope and definition of OS; regional variations in its implementation; the relationship between OS and fundamental questions of the purpose and practice of science; and the need to reform incentives and reward structures within research systems. A more complex understanding of OS is required, which takes into account the importance of equity and diversity and the challenges of implementing OS in different cultural and geographical contexts. The study emphasises the importance of shifting scientific culture to prioritise values such as quality, integrity, and openness, and reforming rewards structures to incentivise open practices.
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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 | 0.199 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.067 | 0.227 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.071 | 0.001 |
| Open science | 0.060 | 0.071 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".