PEDAGOGIES OF THE DATAFIED: MATERIAL FOUNDATIONS FOR LITERACIES OF THE SUBJECT IN THE 21ST CENTURY
Bibliographic record
Abstract
What it means to be human, relevant and meaningful is no longer certain within emerging regimes where computational complexity and data analysis increasingly determine conditions of prosperity and authority. Preparing students for futures within this transforming landscape of emerging technologies and new patterns of social organization raises important issues of literacy, power and subjectivity alike. If the hailing mechanisms of the subject are largely modulated through digital and algorithmic protocols, what kinds of literacies might help expand individual and group influence over subject formation? Traditional approaches to digital literacy have tended to overlook techno-material aspects of network functionality, which risks diminishing the degree to which individuals and groups can extend influence over subject formation. This paper argues for an expanded approach to digital literacy that addresses the techno-material foundations and full range of computational protocols on which network societies depend. Learning to navigate and manipulate the material-discursive apparatus in network societies can help individuals and groups apperceive assemblages of biopower while expanding possibilities for shaping subjectivities in datafied contexts.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".