Identifying the challenges in implementing open science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Areas of open science (OS) policy and practice are already relatively well-advanced in several countries and sectors through the initiatives of some governments, funders, philanthropy, researchers and the community. Nevertheless, the current research and innovation system, including in the focus of this report, the life sciences, remains weighted against OS. In October 2017, thought-leaders from across the world gathered at an Open Science Leadership Forum in the Washington DC office of the Bill and Melinda Gates Foundation to share their views on what successful OS looks like. We focused on OS partnerships as this is an emerging model that aims to accelerate science and innovation. These outcomes are captured in a first meeting report: Defining Success in Open Science. On several occasions, these conversations turned to the challenges that must be addressed and new policies required to effectively and sustainably advance OS practice. Thereupon, in this report, we describe the concerns raised and what is needed to address them supplemented by our review of the literature, and suggest the stakeholder groups that may be best placed to begin to take action. It emerges that to be successful, OS will require the active engagement of all stakeholders: while the research community must develop research questions, identify partners and networks, policy communities need to create an environment that is supportive of experimentation by removing barriers. This report aims to contribute to ongoing discussions about OS and its implementation. It is also part of a step-wise process to develop and mobilize a toolkit of quantitative and qualitative indicators to assist global stakeholders in implementing high value OS collaborations. Currently in co-development through an open and international process, this set of measures will allow the generation of needed evidence on the influence of OS partnerships on research, innovation, and critical social and economic goals.
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.
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 | 0.090 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.068 | 0.091 |
| Open science | 0.070 | 0.133 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it