Malthus at the Movies: Science, Cinema, and Activism around Z.P.G. and Soylent Green
Bibliographic record
Abstract
This article investigates cinema's engagement with the Malthusian movement to control global overpopulation in the long 1960s.It examines the contested production and reception of Z.P.G.: Zero Population Growth (Michael Campus, 1972) and Soylent Green (Richard Fleischer, 1973) to shed new light on the nexus of science, activism, and the media.It argues that the history of the movement, usually reconstructed as an elite scientifi c and political discourse, cannot be fully understood without also taking into account mass-market entertainment. T he Science-Activism-Media Nexus.In the early 1970s, two Hollywood fi lms portrayed the coming millennium as desperately overcrowded and polluted, fast running out of resources and space.Both Z.P.G.: Zero Population Growth (Michael Campus, 1972) and Soylent Green (Richard Fleischer, 1973) were products of the "Malthusian moment," a brief peak in the late 1960s and early 1970s of environmental concerns with world population growth and its control, but they also diff ered in signifi cant ways. 1 Z.P.G. imagined a totalitarian state that banned childbirth on penalty of death, fared as poorly at the box offi ce as with critics, and became embroiled in a major fracas with the grassroots organization Zero Population Growth (ZPG).Soylent Green envisioned a powerful corporation that perpetuated mass cannibalism, performed well at the box offi ce, and generally satisfi ed activists as a politically neutered, if passably ecological, "message" fi lm.Although Malthusian environmentalism was as much about birth control as the biosphere, scholars have generally framed both fi lms in terms less of reproductive politics than of environmentalism.As early as 1978, Joan Dean's infl uential 1 See Thomas Robertson, The Malthusian Moment: Global Population Growth and the Birth of American Environmentalism (New Brunswick, NJ: Rutgers University Press, 2012).Jesse Olszynko-Gryn is a Chancellorʼs Fellow and lecturer at the University of Strathclyde.His fi rst book, A Woman's Right to Know: A History of Pregnancy Testing in Britain, recovers the contested rise of a little-studied technology from around 1900 to the present day.Patrick Ellis is a Marion L. Brittain Postdoctoral Fellow at the Georgia Institute of Technology.His book Aeroscopics: Media Archaeology of the Bird's-Eye View provides a history of aerial vision in the era before commonplace fl ight.
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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 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".