From Tech Lash to Tech Fash: Strategic Reflections on a Decade of Collective Organizing in Computing
Bibliographic record
Abstract
Computing is a field plagued with presentism, oriented towards the new in ways that limit our design and research practices -as well as our capacity to understand and collectively respond to emerging crises.To improve our sensemaking and strategizing about today's crises, this workshop explores what Tamara Kneese has deemed the last decade's shift from "techlash" to "tech fash."What have we learned from the era of misinformation and bias, of "surveillance capitalism" and tech worker organizing that can inform our struggle against the increasing power of a techno-fascist oligarchy?We will also look towards previous generations of computing professionals and activists, who likewise sought to address the harms of emerging automated systems and the complicity of computing within violent, imperialist projects.This workshop will create space for participants to explore these questions collectively, bridging past and present moments in an effort to devise strategies moving forward.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.058 |
| Scholarly communication | 0.031 | 0.025 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".