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Record W2992451903 · doi:10.18357/bigr11201919244

Bordering the Future? The ‘Male Gaze’ in the Blade Runner Films and Originating Novel

2019· article· en· W2992451903 on OpenAlexvenueno aff
Kathleen Staudt

Bibliographic record

VenueBorders in Globalization Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeHollywoodGazeMeaning (existential)AestheticsSociologyArtGender studiesHistoryLiteraturePsychoanalysisPsychologyArt historyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Philip K. Dick (1928-1982), author of numerous science fiction narratives from the 1950s-1980s, some of which Hollywood made into films, grappled with the nature of reality, the meaning of humanness, and border crossing between humans and androids (called ‘replicants’ in the films). The socially constructed female and male protagonists in these narratives have yet to be analyzed with a gender gaze that draws on border studies. This paper analyzes two Blade Runner films, compares them to the Philip K. Dick (PKD) narrative, and applies gender, feminist, and border concepts, particularly border crossings from human to sentient beings and androids. In this paper, I argue that the men who wrote and directed the films established and crossed multiple metaphoric borders, but wore gender blinders that thereby reinforced gendered borders as visualized and viewed in the U.S. and global film markets yet never addressed the profoundly radical border crossing notions from PKD.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0070.005
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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 designTheoretical or conceptual
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

Citations3
Published2019
Admission routes1
Has abstractyes

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