Where’s the database in digital ethnography? Exploring database ethnography for open data research
Bibliographic record
Abstract
Contemporary cities are witnessing momentous shifts in how institutions and individuals produce and circulate data. Despite recent trends claiming that anyone can create and use data, cities remain marked by persistently uneven access and usage of digital technologies. This is the case as well within the emergent phenomenon of the ‘smart city,’ where open data are a key strategy for achieving ‘smartness,’ and increasingly constitute a fundamental dimension of urban life, governance, economic activity, and epistemology. The digital ethnography has extended traditional ethnographic research practices into such digital realms, yet its applicability within open data and smart cities is unclear. The method has tended to overlook the important roles of particular digital artifacts such as the database in structuring and producing knowledge. In this paper, we develop the database ethnography as a rich methodological resource for open data research. This approach centers the database as a key site for the production and materialization of social meaning. The database ethnography draws attention to the ways digital choices and practices—around database design, schema, data models, and so on—leave traces through time. From these traces, we may infer lessons about how phenomena come to be encoded as data and acted upon in urban contexts. Open databases are, in other words, key ways in which knowledges about the smart city are framed, delimited, and represented. More specifically, we argue that open databases limit data types, categorize and classify data to align with technical specifications, reflect the database designer’s episteme, and (re)produce conceptions of the world. We substantiate these claims through a database ethnography of the open data portal for the city of Calgary, in Western Canada.
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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 | 0.084 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.025 | 0.046 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| 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".