Bibliographic record
Abstract
Municipal open data platforms are currently caught in a range of tensions. They rely on an unspecified subject to analyze the data, and yet are surrounded by discourses of "empowerment" and "transparency". They are often most beneficient when approached with data science skills, yet often entail unremunerated digital labor. And they are often engaged by organizations tacking "for Social Good" onto their mandate - the Canada-wide organization Data for Good being a key example. To date, STS research has generated important insights into the political economies of data and platforms that highlight the ways they produce, mediate, circulate, and accumulate surplus and exchange value. Less attention has been devoted to understanding the ways moral values and sentiments are deployed to attract the digital volunteered labor subtending municipal open data platform usage. Those who mobilize these moral economies are deeply situated within capitalist platform economies, and benefit from the free labor of those wishing to improve their communities. In this presentation, we argue that hackathons, datathons, and open data platforms are constituted through moral economies that are entangled within technoscientific capitalist accumulation practices and logics. These moral economies are key ways in which digital labor is procured, and represent a core component of what Boltanski and Chiapello call the "new spirit of capitalism". To substantiate our argument, we draw on an ongoing long-term ethnography into Calgary, Alberta's open data ecosystem. We conclude by politicizing the fissures of these moral economies, to identify the new political strategies that they necessitate.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.062 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 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".