Platforms as laboratories of the social: How digital capitalism matters for computational social research in North America
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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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".