PUSHING BACK: DIGITAL RESISTANCE AS A SENSITIZING CONCEPT
Bibliographic record
Abstract
This paper thus aims to contribute to media, communication, and digital technology studies by proposing a more systematic conceptualization of digital resistance. While the notion of resistance in relation to technology is often connoted negatively and associated the rejection of innovation of change, our approach to digital resistance takes here a new meaning: political and critical. Indeed, the notion of digital resistance is often used in academia and public discourse to describe practices of using, subverting, and creating technologies, usually in a progressive and anti-oppressive perspective (Russell, 2005). However, the term is still relatively undefined, and many practices could be categorized as digital resistance if the term was better defined. We propose in this paper a preliminary but formal conceptualization of digital resistance. Our theorization takes place in the context of a research project on the cartography of digital resistance. Different data collection and analysis activities will be implemented to have a wide and panoramic empirical view of the phenomenon of digital resistance. In this project, the cartographic approach takes on a dual meaning, namely a broad and systematic description of a phenomenon, and the implementation of an original digital device allowing its visualization and potentially participatory enrichment. Our preliminary empirical mapping identified six dimensions to analyze digital resistance that we will present in this paper.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| 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".