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
Malicious hackers profit from the division of labour among highly skilled associates. However, duplicity and betrayal form an intrinsic part of their daily operations. This article examines how a community of hackers uses an automated reputation system to enhance trust among its members. We analyse 449,478 feedbacks collected over 27 months that rate the trustworthiness of 29,985 individuals belonging to the largest computer hacking forum. Only a tiny fraction of the forum membership (2.4%) participates in the vast majority (75%) of ‘trust exchanges’, limiting its utility. We observe a reporting bias where the propensity to report positive outcomes is 2.81 times greater among beginner hackers than among forum administrators. Reputation systems do not protect against trust decay caused here by the rapid expansion of the community. Finally, a qualitative analysis of 25,000 randomly selected feedbacks indicates that a diverse set of behaviours, skills and attitudes trigger assessments of trustworthiness.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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