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Record W3179828930 · doi:10.32920/ryerson.14663961.v1

An exploration of Lara Croft: the good, the bad, and the ugly

2021· preprint· en· W3179828930 on OpenAlexaff
Laura Bacigalupo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsRelevance (law)Interpretation (philosophy)Video gameCharacter (mathematics)SociologyGeorge (robot)GazeAestheticsFocus (optics)Media studiesPsychologyArtPsychoanalysisArt historyPhilosophyComputer scienceMultimediaPolitical scienceLinguistics

Abstract

fetched live from OpenAlex

"In this paper, I explore the character Lara Croft from the Tomb Raider game and affiliated film series through the lens of several feminist theories. The interpretation of Croft by the game's player as either a positive or a negative role model is challenging and presents an opportunity to participate in a continuing debate concerning the expected social behaviours of gender. While my focus will be on feminist theories, I also include an exploration of George Herbert Mead's play stage and its relevance to the reception of video games by their audiences. I will use Laura Mulvey's concept of the male gaze, Judith Butler's thesis of gender as a performance, as well as Donna Haraway's concept of the cyborg to answer the question of whether Lara Croft is a benefit or detriment to the progression of women in the video game industry" -- From the introduction.

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)0.003
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.014
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.050
GPT teacher head0.325
Teacher spread0.274 · 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
Published2021
Admission routes1
Has abstractyes

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