Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives. This article seeks to describe and understand the social organization as well as the distribution of recognition in the online community (also known as the warez scene) of hackers who illegally distribute intellectual property online. Method. The data were collected from an online index that curates a list of illegal content that was made available between 2003 and 2009. Sutherland’s notion of behavior systems in crime as well as Boase and Wellman’s notion of network individualism are used to theorize the social organization and the distribution of recognition in the warez scene. These were then analyzed using social network theory. Results. There is a strong correlation between the productivity of the hacking groups and the recognition they receive from their peers. These findings are limited by the lack of data on the internal operations of each hacking groups and by the aggregate nature of the network matrix. Conclusions. We find that hacking groups that make this online community generally have a very limited life span as well as low production levels. They work and compete in a very distributed and democratic community where we are unable to identify clear leaders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it