Sim-Cyberpunk: Serious Play, Hackers and Capture the Flag Competitions
Bibliographic record
Abstract
Capture the flag (CTF) is a style of game developed within the hacker community to simulate/emulate the practice of vulnerability research. In a CTF players identify security vulnerabilities in information systems and exploit these flaws to undermine their operations, which gives them access to a “flag” which they score for points used to win a competition. An exploratory study of this game, this dissertation uses ethnographic methods including observation of three CTF competitions and semi-structured interviews with 47 CTF players and designers. Analysis of this data considers the co-constitution of the game through the practices of its designers and players, concerning the values of the hacker community and its linkages to the information security industry whose membership constitutes the preponderance of CTF participants. Utilizing Sara Grimes and Andrew Feenberg’s (2009) theory of “games as sites of social rationalization” this paper argues that CTF has been instrumentalized as a tool of cultural reproduction. This function of CTF is used to discursively shape and sustain knowledge acquisition, identity formation and work in the cybersecurity industry through the affordances of play in alignment with hacker values, translating intellectual capital into social capital through playful game systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".