Bordering the Future? The ‘Male Gaze’ in the Blade Runner Films and Originating Novel
Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".