The social life of digital methods in psychology: Situating digital methods in the new data politics
Bibliographic record
Abstract
Abstract In this paper I present some preliminary analyses of what is at stake in the growing use of digital methods in psychology. Their exponential rise in the discipline has scientific consequences, because such methods embody unarticulated assumptions that derive from their cultural, technical, or commercial origins. Such methods also rearticulate researcher‐participant relations in new ways, and reframe what it means to take part in psychological research. Additionally, strong calls for psychologists to exploit the potentials of Big Data to analyze and influence human action in real time signal the growing entanglement of psychology, computer science, and the accumulative tactics of the digital economy. As part of a larger project to trace the social life of digital methods in psychology, my aim here is to link disparate literature in psychology, science and technology studies, and critical data studies, to situate such methods in the broader context of technologically afforded shifts in modes of economic and knowledge production. I argue that digital methods are in urgent need of analysis, not only in terms of the interpretive frames, modes of participation, and courses of action they afford, but as research media that circulate in a larger digital and political economic ecosystem, and with associations that span multiple sociotechnical assemblages.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".