Bibliographic record
Abstract
This entry explores intersectional data analysis rooted in social justice in the social sciences and humanities. The datalogical turn foregrounds the proliferation of algorithmic processing and data as an emergent regime of power/knowledge in the digital datafication of everyday life. Big Data, digital methods, and data studies are buzzwords that privilege modes of knowledge production to elevate quantitative, abstracted, and disembodied approaches over qualitative data approaches. However, database technologies and human experiences are always necessarily mutually constituted. Infrastructures, categorizations, and algorithmic processing are commonly black‐boxed and therefore invisible with the consequence that data generated is never raw, but always cooked. These processes are not devoid of different forms of cultural prejudices and discriminations, rather they are often used to exacerbate gendered, sexed, racialized, and classed power hierarchies. Subtopics to be discussed in this entry include feminist ethics of care and alternative data studies; examinations of digital infrastructures including e‐waste and assemblages of hardware and software; and critiques of data gathering and data visualizations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".