Platforms as laboratories of the social: How digital capitalism matters for computational social research in North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The contemporary prevalence of artificial intelligence and machine learning methods has resulted in a rich literature on the factors that shape computational research. This article draws on the laboratory studies literature to examine how platforms' socio-technical infrastructures shape contemporary computational social science research. Based on 18 months of online ethnography of a university laboratory and 15 in-depth interviews with its researchers, the article makes two main arguments. First, for computational social sciences, platforms function as laboratories where the social is selectively carved and transformed, to make it knowable with computational methods. Thus, it makes the case that platforms manufacture the objects of analysis in computational social research and provide the social as a domain. Second, because of the significance of social media platforms as data laboratories for computational research, in contrast to the claims of data sciences to be domainless, these sciences may derive some of their epistemological and occupational power, as well as their cultural authority, from digital capitalism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it