'We're not making it as easy as we might': issues and opportunities Open Science mandates present to Canadian ethics officers and librarians supporting human ethics
Bibliographic record
Abstract
Background: Open Science (OS) offers a grand challenge by demanding both cultural and structural change across the ecosystem to realize global benefit and research accountability. The regulatory function of ethics review for human participatory research (HPR) ensures protection of the participant throughout the entire research project. With OS mandates, the review process and its stakeholders are located at the centre of this challenge: upholding the values of OS while also defending participant autonomy and privacy, community stewardship and situational policy obligations, as well as evaluating and managing other risks. Data management plans (DMPs) reflect all these aspects in a project. Purpose: To explore the experiences of supporting research data management (RDM) and/or DMPs as part of ethics review process by ethics officers, chairs and librarians and/or staff. Methodology: Eight semi-structured, individual interviews were conducted and audio recorded by the author. Transcripts were analyzed using a critical realist approach to identify patterns of support considerations. Analysis/Findings: An inter-related grouping of themes arose from participant reflections in their organizations: time, capacity, personal v. system-constructed relationships. Time, and its consumption, was the determining factor in both providing access to support and demonstrating its value. Definitions of capacity extend beyond infrastructure limitations and availability to require a collective knowledge and awareness across all organizational levels. Relationships are the ‘engine’ of capacity, and in the absence of system-identified networks, the efficacy of support is dependent on the provider’s agency, leadership mobilization, and ‘known quantity’ within the organization. These themes, informed by the participant’s function and positionality within their organization, together contributed to an overall thematic perception of paralysis v. engagement with respect to data management plan adoption and utility, and an ‘us against the system’ dynamic. Data management topics of data sharing, deposit and sovereignty were issues of specific concern. Conclusions: To better meet the complex, interdisciplinary challenges of OS-related data management-ethics concerns, a movement from solo or isolated units of support, defined by process or discipline, to new matrixed system structures which facilitate role awareness, knowledge exchange, and common language should be encouraged.
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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 | 0.090 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.079 | 0.084 |
| Scholarly communication | 0.042 | 0.016 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".