Civic Participation in the Datafied Society| Citizen Data Audits in the Contemporary Sensorium
Bibliographic record
Abstract
Citizen data audits build on Jesús Martín-Barbero’s (JMB) theorization of the contemporary sensorium to foreground citizens’ situated, affective responses to datafication. We argue that social audits are necessarily historically situated, highlighting how the processes by which we evaluate reality or think about data power are inevitability contextually bound. With this in mind, JMB’s maps of contemporary mediations ground local experiences with datafication; however, we argue that his work can be complemented by more nuanced appreciations of affect theory. Based on this, we discuss three citizen-centered data audits techniques that can be used to encourage personal assessments of engagements with the contemporary sensorium. Overall, this work offers individuals and communities methods to analyze, reflect on, and evaluate their unique, contextual engagements with datafied and algorithmic societies. These methods offer people pathways to visualize and redraw the map of the systems they inhabit—and possibly even to reposition themselves within it.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".