Contrasting the open access dissemination of COVID-19 and SDG research
Bibliographic record
Abstract
Abstract This paper examines the extent to which research has been published open access in response to two global threats: COVID-19 and the Sustainable Development Goals (SDGs), including climate change. We compare the accessibility of COVID-19 content versus SDG literature using the Dimensions database between 2000 and 2021, classifying each publication as gold open access, green, bronze, hybrid, or closed. We found that 79.9% of COVID-19 research papers published between January 2020 and December 2021 was open access, with 39.0% published with gold open access licenses. In contrast, just 55.7% of SDG papers were open access in the same time period, with only 36.0% published with gold open access licenses. Papers related to the climate emergency overall had the second-lowest level of open access at just 55.5%. Papers published by the largest for-profit publishers that committed to both the SDG Publishers Compact and climate actions were not predominantly published open access. The paper highlights the need for continued efforts to promote open access publishing to facilitate scientific research and technological development to address global challenges. One-Sentence Summary In contrast to COVID-19 papers, research on UN Sustainable Development Goals including the climate emergency have not been made open access by leading global science publishers despite their corporate commitments to sustainability and climate action.
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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 | 0.065 | 0.354 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.056 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".