When Technocultures Collide: Innovation from Below and the Struggle for Autonomy
Bibliographic record
Abstract
When Technocultures Collide provides rich and diverse studies of collision courses between technologically inspired subcultures and the corporate and governmental entities they seek to undermine. The adventures and exploits of computer hackers, phone phreaks, urban explorers, calculator and computer collectors, “CrackBerry” users, whistle-blowers, Yippies, zinsters, roulette cheats, chess geeks, and a range of losers and tinkerers feature prominently in this volume. Gary Genosko analyzes these practices for their remarkable diversity and their innovation and leaps of imagination. He assesses the results of a number of operations, including the Canadian stories of Mafiaboy, Jeff Chapman of Infiltration, and BlackBerry users. The author provides critical accounts of highly specialized attributes, such as the prospects of deterritorialized computer mice and big toe computing, the role of electrical grid hacks in urban technopolitics, and whether info-addiction and depression contribute to tactical resistance. Beyond resistance, however, the goal of this work is to find examples of technocultural autonomy in the minor and marginal cultural productions of small cultures, ethico-poetic diversions, and sustainable withdrawals with genuine therapeutic potential to surpass accumulation, debt, and competition. The dangers and joys of these struggles for autonomy are underlined in studies of RIM’s BlackBerry and Julian Assange’s WikiLeaks website.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.098 |
| Scholarly communication | 0.036 | 0.020 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.005 | 0.012 |
| 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".