Empirical approaches to infrastructures for datafication: Introduction to the special issue
Bibliographic record
Abstract
This article introduces a special issue exploring emerging empirical approaches to studying infrastructures for datafication and their social, political, and economic implications. The merits of empirical research on infrastructures for datafication are drawn out across seven articles offering diverse methodological entry points to develop our understanding of how datafication processes operate across everyday life settings, sectors, and institutions. The contributions span multiple levels of infrastructural analysis, from tracking ecologies to digital platforms and chatbots. They also cover a range of core questions regarding the relationship and power dynamics between private and public institutions, and between big technology companies and everyday citizenhood. In illuminating how infrastructures for datafication operate, for whom and with what ends, the special issue extends a fruitful dialogue between infrastructure studies and people-centric approaches to datafication and opens avenues for infrastructure research across disciplines to create more coherent understandings of how specific technological operations shape social life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".