Bibliographic record
Abstract
We have framed the theme of this issue as “The Democratization of Hacking and Making” to draw attention to the relationships between action, knowledge, and power. Particularly, hacking and making are about how practices of creation and transformation generate knowledge and influence institutions. These acts concentrate and distribute power through publics and counterpublics. Yet, the very mutability of hacker and maker relations makes them a challenge to identify and research. Hacking and making collectives have proven capable of constituting and reconstituting themselves in physical and virtual spaces. They integrate across infrastructures, collaborative systems, socio-economic divides, and international boundaries. Hacking and making movements are plural and diverse, but in no way are they entirely new. These movements have specific histories, cultures, and traditions. As they quickly sprawl across national and geographic boundaries, they tend to forget those stories and lineages. This function of forgetting also occurs in a cyclical fashion in the public and research communities. The public has become aware of the popularization of hacking and making mostly through moments of emergency and scandal. Forgetting serves a function for the public, allowing them to get on with their own interests. Concurrently, this public forgetting allows hackers to regain their spaces of creativity and action. Maker culture, too, forgets in order to find a perpetual sense of novelty in their very existence. Forgetting, an important social and cultural project, is also part of the democratic project. Democracies forget to put aside old tensions and re-form in order for the public to support them. Thus, although each article considers fundamentally democratic concerns of access, participation, and collaboration, these questions are couched in just one set of
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".