Cultivating legacies and connections: A narrative analysis of Instagram stories of retired elite athlete mothers through an ethic of care lens
Bibliographic record
Abstract
Although significant research has focused on athlete mothers returning to competition, the experiences of retired athlete mothers remain largely unexplored. In this study we explored the less studied research path of motherhood and sport retirement to learn more about these athletes’ lives. We sought to build on sport media research centralizing elite athlete mothers and qualitative research on athlete mother career transitions, to provide insight into identities post-elite sport in a cultural context (i.e., Instagram). The precise aim was to explore how identities intertwined with ethic of care meanings in digital stories and the psycho-social implications during retirement. Two retired Canadian athlete mothers’ (i.e., mountain biker Catharine Pendrel and boxer Mandy Bujold) Instagram posts (n = 72 for Pendrel, n = 162 for Bujold) were subjected to big and small story narrative analysis. A big story of legacy through generativity was identified and linked with ethic of care meanings depending on three small stories: giving back, inspiring the next generation, and self-connections through sport. These findings show how a concern for current and future generations through pro-social behaviors (e.g., philanthropy, imparting wisdom, time with children) are intertwined with multiple relational identities (e.g., elite athlete, mother, mentor, generative athlete) and nuanced ethics of care (e.g., self-care, everyday acts of situated caring). We conclude with what these findings and digital stories offer practitioners, closing with future research suggestions. We used a big and small story approach to explore Instagram posts of athlete mothers to understand identities and ethic of care meanings in retirement. Findings show the value of digital stories to learn more about relational identities grounded in a continuum of caring and pro-social behaviors, and how these assist with sport retirement adjustment. A big and small story approach extends understanding of elite athlete mothers’ career transitions and can stimulate conversations about the content of personal and public stories in digital spaces as resources.To stimulate such conversations, practitioners might explore elite athlete mothers’ posts on social media spaces (i.e., Instagram) by asking questions about retirement adjustment in relation to a ‘generative athlete’ identity. Questions might include, “how is family portrayed and talked about post-sport career?,” “is the athlete showing aspects of their private lives—good or bad—for others to learn from? What lessons are gained from these?”Small stories of giving back, inspiring the next generation, and self-connections through sport that shape a legacy through generativity big story, can also be used as resources to show athlete’s the benefits of relational identities and practices that assist with athlete mothers’ adaptation in retirement. A big and small story approach extends understanding of elite athlete mothers’ career transitions and can stimulate conversations about the content of personal and public stories in digital spaces as resources. To stimulate such conversations, practitioners might explore elite athlete mothers’ posts on social media spaces (i.e., Instagram) by asking questions about retirement adjustment in relation to a ‘generative athlete’ identity. Questions might include, “how is family portrayed and talked about post-sport career?,” “is the athlete showing aspects of their private lives—good or bad—for others to learn from? What lessons are gained from these?” Small stories of giving back, inspiring the next generation, and self-connections through sport that shape a legacy through generativity big story, can also be used as resources to show athlete’s the benefits of relational identities and practices that assist with athlete mothers’ adaptation in retirement.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".