Deconstructing Images of Mothering in Media and Film: Possibilities and Trends for the Future
Bibliographic record
Abstract
for the Future In the collision of reality with mythology, it is the mythology that tends to prevail, as the language and the conventions of the story shape not only what is thought but also what can be said, not only what is heard but what can be understood. (Pope, Quinn, and Wyer, 1990: 445) Over the past two decades, feminist researchers have persuasively argued that representations of the mother in popular culture shape our complex feelings about motherhood (Bassin, Honey and Kaplan, 1994). In fact, the "ideology of mothering can be so powerful that the failure of lived experience to validate often produces either intensified efforts to achieve it or a destructive cycle of self- andor mother-blame " (Pope et al., 1990: 442). From June Cleaver to Murphy Brown, television has obviously had a powerful impact on how maternal roles are valued and played out. These images have been well analyzed and deconstructed. In addition, the film industry and Hollywood directors have also had a significant part to play in what we value and expect of motherhood in North America. However, these big-screen mothers have not been as carefully scrutinized as their television counterparts. This paper explores the mother-as-subject as depicted by the film industry over the past 40 years. In particular, it will outline a cross-disciplinary undergraduate course on Mother-ing andMotherhood: Images, Issues and Patterns that I have developed and teach in the Women's Studies program at BrockUniversity, St. Catharines, Ontario,
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.025 | 0.039 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".