Journeys Into Night: Agewise Cinematic Constructions in Cas and Dylan and Our Souls at Night
Bibliographic record
Abstract
Ashton Applewhite, American writer, activist, blogger and expert on ageism, the author of This Chair Rocks: A Manifesto Against Ageism (2016), remarked in her 2017 TED (Technology, Entertainment, Design) series lecture “Let’s End Ageism” that today when the aged population is, according to the United Nations statistics, at its highest level in human history, in most societies, including developing and the developed countries alike, “people are living longer and societies are getting grayer; you read and hear about it on all media platforms and outside of them.” This essay will be about a slice of these platforms tackling cultural narratives involving longevity and ageing―and their subsequently increased visibility on the silver screen. In order to investigate ageing as a marker of life course identities in two cinematic matching and mismatching journeys into ageing, I have chosen two North American movies presented in the past five years, the Canadian-made Cas and Dylan (2013) directed by Jason Priestley and with Richard Dreyfuss and Tatiana Maslany in leading roles, and the US-produced Our Souls at Night (2017), directed by Ritesh Batra, featuring in the main roles Jane Fonda and Robert Redford. I am interested to see the ways in which the representation of senior citizens―in the above-mentioned movies all being members of the North American Baby Boomers generation―is challenging the cultural myths of aging through various acts of performativity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".