Joanna McClelland Glass’ Mrs. Dexter and Her Daily and the Inter-play among Age, Gender and Class
Bibliographic record
Abstract
Following the so-called revolution of longevity, which was initiated after the Second World War, and as a result of recent scientific and social changes, multidisciplinary studies of old age, aging and ageism have increasingly been regarded as a necessity in our contemporary developed -and aged- societies. Consequently, and together with gender, class and ethnicity, age has become a prominent social and cultural marker which is explored by different discourses both as an ever-changing site of identity, and as a potential source of social discrimination. Cultural representations of aging have changed throughout the last decades in order to accommodate the increasingly plural realities whereby old age can be recognized and categorized, thereby contributing to the transformation of ‘age’ as a construct and identity marker. Out of the various artistic and communicative channels whereby old age is constantly re-presented, the theater stands out as an especially enlightening medium through which the constructedness of aging and ensuing stereotypical visions of old men and women can be explored. This paper will analyze the re-presentation of aging and its intersections with the politics of gender and class in Joanna McClelland Glass’ Mrs Dexter and Her Daily (2010). As a naturalistic two-hander which constructs its protagonists through realistic psychological portraits, the last play that this Canadian-American playwright has published to date presents two female characters in their mid-sixties whose old age is not only submitted to “the double standard” through which women’s aging is measured, to borrow from Susan Sontag’s words, but also conditioned by their diverse social positions and distinctive fictional biographies. Using theater semiotics and the latest theories of cultural gerontology as the two main methodological frameworks for this particular case study, this paper hopes to demonstrate that the theater continues to be an invaluable source of inquiry with regard to significant aspects of human life. At the same time, it intends to highlight the centrality of the experience of aging in its interconnection with other identity markers such as gender and class, as well as in the discourses that are derived from them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".