Inclusion, Diversity, Equity, and Accessibility (IDEA) in multi-regional data research: An approach to facilitating change in a distributed research environment.
Bibliographic record
Abstract
ObjectiveA recently established data research network identified the need to address Inclusion, Diversity, Equity, and Accessibility and established a team committed to IDEA informed change. The Team identifies, develops, and implements IDEA strategies in distributed research network. We will share the process of establishing this Team and outline identified coordinated opportunities moving forward. ApproachAdministrative data and analyses are not neutral. Colonialism, racism, gender discrimination, ableism, and other forms of oppression have shaped the data that are available and the research processes that are used to analyze and evaluate data. It is imperative that health data research adopts and develops methods that embed IDEA. An informal survey of a health data research network identified four areas for action including capacity building, culture shifts, information sharing, and coordinated development of policies, practices, and tools. A multi-regional team was established to move forward with this work with an emphasis on operations and research practices. ResultsIn the fall of 2021 IDEA Team members were recruited from data centres across the network, including IDEA professionals, researchers, data collection and curation specialists, human resource professionals, and public/patient engagement specialists. The first IDEA Team meetings focused on team buildings and building shared purpose by shaping the Terms of Reference and creating principles for working together. Next steps will include an environmental audit to assess network capacity and a consensus oriented decision-making process to identify priorities. The Team includes over 20 members with a broad range of knowledge and expertise, making facilitation a key operational component. Establishing a baseline of knowledge and defining priorities will encompass the first year of the Teams work, navigating the distinct needs of a distributed network, the diverse needs of data centres, and the breadth of data that flows through the network. ConclusionThis multi-regional inter-disciplinary team is crucial to adopting, developing and embedding IDEA in a distributed data network and could serve as a model for other data research organizations. The Team will work towards capacity building, creating an internal culture shift and coordinating the development of new tools and resources.
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 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 | 0.368 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.022 | 0.049 |
| Scholarly communication | 0.029 | 0.035 |
| Open science | 0.007 | 0.087 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".