Built Women in Men’s Paradises: A Critical Analysis of the Garden of Eden Narrative and Alex Garland’s <i>Ex Machina</i>
Bibliographic record
Abstract
Bringing the biblical story of the Garden of Eden (Genesis 2-3) into conversation with Alex Garland’s 2014 film Ex Machina, this paper examines and compares the male-scribed nature of paradise stories that describe the “building” of woman-creatures. From ancient Judean scribes to modern film-makers and computer coders, male-guarded forms of literacy enabled and continue to enable storytelling and world-building. A comparison of the accounts of the creation of Eve of the Garden with Ava of Ex Machina highlights that male control over literacy more generally and creation accounts more specifically yields diminished woman-creatures designed to serve the specific needs of men in male-imagined paradise settings. Although separated by millennia, ancient Judean scribes and modern computer programmers have imagined and built woman-creatures with a limited set of functions and programmed routines that include providing help, serving as a companion, and heterosexual receptivity.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".