Clicks and particulates: Value, alienation, and attunement as unifying themes in big data studies
Bibliographic record
Abstract
Critiques of data colonialism and surveillance capitalism focus on data collected from online behavior. We propose that analytical concepts from these critiques—namely, regimes of value and patterns of alienation and attunement—could be applied more widely to better understand the threats that datafication poses to equity and democracy in the social and environmental realms. Regimes of value, which include the institutions and technologies that make data meaningful and render them selectively available for appropriation, are relevant both to for-profit companies’ data practices and to states’ participation in the datafication of the environment; examining regimes of value raises questions about how data are exploited and how they are neglected. Patterns of alienation associated with datafication include the potential for alienation from the environment; however, at least in some value regimes, alienation may be accompanied by possibilities for attunement to natural and social phenomena that might otherwise have escaped notice.
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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 | 0.069 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.012 | 0.167 |
| Scholarly communication | 0.030 | 0.067 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".