Do We need Data Literacy? Public Perceptions Regarding Canada’s Open Data Initiative
Bibliographic record
Abstract
Open Data is a new concept that the Canadian Government is using to encourage civic engagement, to promote economic growth, and as a means of supporting transparency and accountability within the government. Our research addresses the extent to which the Canadian Open Government initiative, specifically the publishing of open data, has impacted the general public. While emerging research on open data suggests that there is a problem with data literacy levels among citizens, it does not acknowledge the public’s opinion about the relevance of data literacy and open government to their own lives. In order to address this gap, we gathered 42 responses from an anonymous electronic survey that employed both qualitative and quantitative methods to assess the opinions of a portion of the Canadian public. We discovered that there are several factors that enable or impede the initiative’s ability to achieve its stated goals of transparency, accountability, and collaboration for the general public—the public’s data literacy levels, clarity of the data, and awareness of the initiative are a few of the most prominent. The results of the study provide further insight into the public’s opinion on open data, their perceived data literacy skills, and the impact the open data initiative has on their lives. First Place DJIM Best Article Award.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".