CONSENT AS “FEELING-WITH”: EVERYDAY AUTOMATION AND ONTOLOGIES OF CONSENT IN A COMMUNITY TECHNOLOGY CENTRE
Bibliographic record
Abstract
In this article we investigate how automation structures consent for people, and the pedagogies that might expand or reframe consent, both in research contexts and on digital platforms. We do so as researchers involved with adults who attend a bi-weekly drop in “computer support” café, many of whom are new to computers even as their lives are increasingly organized by automated agencies. We think with theories (Jackson and Mazzei 2012) of Indigenous relationalities (Maynard and Simpson 2022), feminist approaches to sexual consent (Consentful Tech Project 2020; Siggy 2021; Ward 2019); and concepts of individuation and technicity (Simondon 1958; 2005) to explore what consent might entail if it returned to its etymological origins of consent, or feeling-with. In keeping with the theory-as-method approach we re-imagine two modes of consent, that of our university’s informed consent protocol for researchers, and the consent protocol for a government sponsored electronic ID. We find that both converge in the logics of corporate platform design that incentivize “epistemologies of ignorance” (Bhatt and MacKenzie 2019). We conclude with speculations upon how con-sent as feeling-with might be re-animated in research methods, design and pedagogy that foreground ethical relationality.
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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 | 0.041 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.095 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".