Cultural and institutional barriers among data stewards regarding data access for research
Bibliographic record
Abstract
IntroductionIn British Columbia, the rules and procedures that data stewards follow to adjudicate data access requests (DAR) vary considerably. These variations can lead to discrepancies in the speed at which DARs are processed. With complex DARs involving numerous data stewards and data sets, the request may take over a year Objectives and ApproachOur main goal was to understand the institutional and cultural factors that influence data stewards when processing a DAR. We wished to see in particular if risk aversion was playing a role when making decisions about data access. We interviewed 24 people representing 21 organizations in British Columbia. Most were data stewards, but we also interviewed people processing the data requests and also privacy advisors. ResultsWe found that organizations varied greatly in terms of their skills and expertise regarding the rules and procedures around processing DARs. In particular, data stewards noted that they experienced differences in interpreting legislation, resulting in disagreements when they were working with other data stewards. In terms of risk aversion, data stewards stated they wished to encourage research, but in some cases followed unclear rules. Nearly all noted that there is little guidance provided for the job of “data steward” and either no or very little training when taking on these positions. Conclusion/ImplicationsWhile there may be stated governmental policies promoting that linked data be used for research, ultimately it is the data stewards approving DARs that will determine access to data. Understanding how and why they make those decisions will help better implement data access policies.
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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 | 0.042 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".