Open scholarship in Australia: A review of needs, barriers, and opportunities
Bibliographic record
Abstract
Abstract Open scholarship encompasses open access, open data, open source software, open educational resources, and all other forms of openness in the scholarly and research environment, using digital or computational techniques, or both. It can change how knowledge is created, preserved, and shared, and can better connect academics with communities they serve. Yet, the movement toward open scholarship has encountered significant challenges. This article begins by examining the history of open scholarship in Australia. It then reviews the literature to examine key barriers hampering uptake of open scholarship, with emphasis on the humanities. This involves a review of global, institutional, systemic, and financial obstacles, followed by a synthesis of how these barriers are influenced at diverse stakeholder levels: policymakers and peak bodies, publishers, senior university administrators, researchers, librarians, and platform providers. The review illustrates how universities are increasingly hard-pressed to sustain access to publicly funded research as journal, monograph, and open scholarship costs continue to rise. Those in academia voice concerns about the lack of appropriate open scholarship infrastructure and recognition for the adoption of open practices. Limited access to credible research has led, in some cases, to public misunderstanding about legitimacy in online sources. This article, therefore, represents an urgent call for more empirical research around ‘missed opportunities’ to promote open scholarship. Only by better understanding barriers and needs across the university landscape can we address current challenges to open scholarship so research can be presented in usable and understandable ways, with data made more freely available for reuse by the broader public.
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 | 0.042 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.030 | 0.062 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".