Introduction: Probing the System: Feminist Complications of Automated Technologies, Flows, and Practices of Everyday Life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This Special Section presents diverse scholarly voices examining the silenced, underexposed, intersectional forces that fortify science and technology platforms in their work to automate public abidance. The articles probe, from diverse global locations and perspectives, the contemporary work of various “platforms,” understood broadly as technology and software, health, social media, and policy platforms. The articles probe these systems and platforms with attention to the assumptions and practices embedded in their algorithms, protocols, design specifications, and communications, and, in turn, the political, cultural, governance, and mediated practices they make possible. The research studies and practice-based work herein expose the complex and shifting sociopolitical codes and contexts that condition technology, artificial intelligence (AI), surveillance, health, social media, and state platforms that support systems of care, news, communication, and governance. These exposures show how platform craftiness works differently in different spaces to privilege and damage, often with ghostly obscurity. Attentive to how platforms operate in complex contemporary viral modes, the section seeks to locate and expose these traces, draped in what communication scholars Sangeet Kumar and Radhika Parameswaran (2018, 345) refer to as “chameleon cultural codes” that, in changing and transforming into unrecognizable forms, feed global imaginaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it