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
The Association of Research Libraries (ARL)/Canadian Association of Research Libraries (CARL) Joint Task Force on Research Data Services (RDS) formed in 2020 with a two-fold purpose: (1) to demonstrate and commit to the roles research libraries have in stewarding research data and as part of institution-wide research support services and (2) to guide the development of resources for the ARL and CARL memberships in advancing their organizations as collaborative partners with respect to research data services in the context of FAIR (findable, accessible, interoperable, and reusable) data principles and the US National Academies’ Open Science by Design framework. Research libraries will be successful in meeting these objectives if they act collectively and are deeply engaged with disciplinary communities. The task force formed three working groups of data practitioners, representing a wealth of expertise, to research the institutional landscape and policy environment in both the US and Canada. This report of the ARL/CARL RDS task force’s working group on partnerships highlights library RDS programs’ work with partners and stakeholders. The report provides a set of tools for libraries to use when assessing their RDS partnerships, including assessing partnerships using a partnership life cycle, defining the continuum of possible partnerships, and creating a catalog. Not all partnerships will last the entirety of a librarian’s career, and having clear parameters for when to continue or sunset a partnership can reduce ambiguity and free up resources. Recognizing the continuum of possible partnerships can provide the framework by which librarians can understand the nature of each group. From cyclical to seasonal to sporadic, understanding the needs of a type of partnership can help libraries frame their understanding and meet a group where they are. Finally, creating a catalog of partnerships can help libraries see the landscape of the organization, as well as areas for growth. This approach also aligns with OCLC’s 2020 report on Social Interoperability in Research Support: Cross-Campus Partnerships and the University Research Enterprise, which highlights the necessity of building and stewarding partnerships. Developing and providing services in a decentralized organization relies on the ability to build trusted relationships. These tools will help libraries achieve sustainable growth that is in concert with their partners, generating robust, clearly aligned initiatives that benefit all parties, their campuses, and their communities.
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 | 0.053 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.011 | 0.049 |
| Open science | 0.056 | 0.107 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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