Making our world : the hacker and maker movements in context
Bibliographic record
Abstract
Jeremy Hunsinger/Andrew R. Schrock: Introduction - Andrew R. Schrock: Section I: Histories Introduction - T. Philip Nichols/Debora Lui: Learning by Doing: The Tenuous Alliance of the and Education Reform - Molly R. Sauter: Kevin Mitnick, The New York Times, and the Media's Conception of the Hacker - Yasuhito Abe: Making Civic Media in the Post-Fukushima Japanese Media Ecology - Rhea Vichot: Project Chanology and the Formation of Anonymous as an Activist Movement - Andrew R. Schrock: Section II: Politics Introduction - Nathanael Bassett: Conscientious Hacking and the Weak Collective - Arne Hintz: Policy Hacking: Opening Up the Code of Media and Communications Regulation - Morgan Currie: Hacking Administration-A Report From Los Angeles - Sebastian Kubitschko: Why Locality and Presence (Still) Matter for Political Activism - Jeremy Hunsinger: Section: III: Organizing Introduction - Alexander von Lunen: Basteln, Tinkering, and Bricolage: A Cultural History of Hacking - Jennifer Maher: Women's Hacking of the Poison Gift of Free/Libre/Open Source Software - Alison E. Vogelaar/Charlotte M. McKernan: Making Space for a Revolution: Occupy Wall Street as a Maker Movement - Ann Light: The Detente Model of Managing Divergent Values in the Maker-Sphere - Jeremy Hunsinger: Section IV: Case Studies Introduction - Pip Shea: Hacker Agency and the Raspberry Pi: Informal Education and Social Innovation in a Belfast Makerspace - Nicholas Balaisis: Hacking as a Way of Life: at the Margins of Global Digital Culture - Xin Gu: The Paradox of Maker Movement in China - Karen Louise Smith: Our Community Hacks: Exploring Hive Toronto's Open Infrastructures - Andrew R. Schrock: Afterword: Hackers and Makers are Ordinary.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".