Early Retirement, Social Class, and Family Relationships in Cloutier’s Bonne retraite, Jocelyne (2018)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Aging women are continuously underrepresented in performing arts. An exception to this trend in Québec is Fabien Cloutier's latest play Bonne retraite, Jocelyne (2018), whose protagonist is a middle-aged woman who has decided to take early retirement. This article aims at examining the interplay between the protagonist's voluntary early retirement, gender, social class, and family relationships. RESEARCH DESIGN AND METHODS: This article mobilizes the theoretical framework of aging studies, which is underdeveloped in the French academic sphere. This qualitative case study connects gender issues and performative strategies in order to study a complex phenomenon within its context. RESULTS: We showed that Cloutier provides new representations of middle-aged women in his play, a corrective to the under-representation of such an age group of actors and actresses in Québec. Our results cast a new light on the combination of class privilege, gender, and ageism, as a most fruitful research orientation to be further developed in the future in order to pursue an in-depth analysis of early retirement. DISCUSSION AND IMPLICATIONS: The play reveals that belonging to the upper-middle class is essential in order to be able, economically speaking, to retire early. Cloutier empowers Jocelyne both as an aging individual and as a woman by means of granting her the right to make her own decisions in life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".