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Record W4384695195 · doi:10.22215/etd/2023-15559

Computer Hacking Culture in Late 20th Century American Teen Film

2023· dissertation· en· W4384695195 on OpenAlexaff
Michael Alan Douglas

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsCarleton University
FundersEli Lilly and Company
KeywordsHackerCountercultureNarrativeCybercultureGeniusRealmMedia studiesThe InternetInternet privacySociologyComputer securityHistoryArtPolitical scienceLiteratureArt historyWorld Wide WebComputer scienceLaw

Abstract

fetched live from OpenAlex

The late twentieth century saw lasting changes to society due to the emergence of personal computers and their eventual connection to the Internet.Computer 'hacking' became publicly known as an innocent activity and later a problem resulting in illegal data breaches and infrastructure destabilization.Combining a historical analysis of computing and connectivity in the period from the 1960s through the early 2000s with a close thematic analysis of the three films WarGames (1983), Real Genius (1985), and Hackers (1995), the realm of teen film is used to explore the connection between 1960s American counterculture and the later computer-based cyberculture.It is argued that these particular films differed from their contemporaries because of their lead characters, their social groups, and the societal change they represented.These three enduringly popular films provided increasingly diverse portrayals of young people who saw the future potential for personal computers and network communications.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.013
GPT teacher head0.306
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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