Open government data (OGD): challenging the concept of a “Designated Community”
Bibliographic record
Abstract
Purpose This paper aims to explore the curation of government-produced datasets for release as open government data (OGD) from the perspective of the digital curation and preservation concept of a “Designated Community”. Specifically, it explores how digital curation functions when there is no clear Designated Community to which curation services can be targeted. Design/methodology/approach The research was conducted through a case study of the City of Toronto’s efforts to revitalize their OGD program. Data was collected using three methods: semi-structured interviews, non-participative observation and document analysis. Findings The curators of OGD responded to the absence of a Designated Community through two complementary methods. The first was to draw from the discourse that defines the OGD domain. The second was to take a participatory approach that incorporated members of the community surrounding OGD and various other stakeholders into the process of developing a plan for the revitalization of the program. Research limitations/implications This study opens new directions for investigating the application of the Designated Community concept and its role in digital curation and preservation. Practical implications The approach used by OGD curators in this case has the potential to be used in other curation situations where there is no clearly defined user group. Originality/value The findings presented in this paper contribute empirical insights to on-going discussions on the concept of a Designated Community in digital curation and preservation.
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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 | 0.065 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.024 | 0.095 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".