The unhomed data subject: negotiating datafication in Latin America
Bibliographic record
Abstract
Critical scholarship about datafication reveals the implications of algorithmically driven digital transformations for both social processes and human experiences of subjectivity. Digital transformations embed ontological beliefs in the information systems that drive new organizational processes and are accompanied by techno-positivist discourses that promote the benefits of these schemes. The dual power of new information systems plus strong discursive influences has led to fears that data subjects will come to be defined by data and information systems – that their subjectivity will be subordinated by the algorithm. However, in this paper, we argue that real experiences of data sharing offer a means to reveal actual experiences with subjectification, and that often these experiences are multiple and complex. Drawing on the results of five digital literacy interventions carried out by partner organizations in Chile, Colombia, Paraguay, Peru, and Uruguay in 2021, we consider participants’ lived experiences with datafication. Our work reveals how people experience, negotiate, reject, and accept data power’s multiple manifestations in ways that strategically mobilize data resources, constituting a fractured data subjectivity that overlaps the bounds of any one information system. This leads us to suggest the idea of the ‘unhomed’ as a useful concept for understanding data subjectification in the contemporary moment.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 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 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".