Words of wisdom from mother runners: Using creative non-fiction advice letters to show psycho-social tensions and strategies influencing performance
Bibliographic record
Abstract
Although motherhood is a well-documented time when recreational sport pursuits decline, researchers have shown that some women participate in sport after becoming mothers. Recreational running is one sport where mothers (re)negotiate their subjectivity in ways that resist constraining good mother ideals by expanding strategies to enhance well-being. There remain nuanced tensions in this process that are less understood, particularly in terms of how mother runners negotiate training and competing. In this study, we used relativist narrative inquiry to explore these tensions and performance strategies of five North American competitive mother runners with young children, theorized as stories in cultural narratives. We used thematic narrative analysis to identify a theme of patience with the process: it's a long and winding road . We then shifted to storytellers to present the meanings of this theme as three accessible creative non-fiction (CNF) letters of advice to other potential mother runners. Advice letters outlined strategies pertaining to the physical self, a flexible mindset and social support . We reflect on the central theme in relation to narratives of good motherhood, sport performance, and discovery, and the implications for psycho-social tensions and performance strategies. CNF advice letters show the pedagogical potential of different kinds of stories to learn more about the constraining and empowering aspects of sport in mothers’ lives, in cultural context.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".