Carrying stories: digital storytelling and the complexities of intimacy, relationality, and home spaces
Bibliographic record
Abstract
Over the past decade, we have worked alongside storytellers to bring their stories into the world. These encounters have been challenging, exciting, and intimate. In this paper, we reflect on a digital/multimedia storytelling project in which we engaged with people who have experienced weight stigma in fertility, pregnancy, and motherhood care. We use the metaphors of story midwifery and surrogacy to describe the methodological-substantive interplay between what we do, how we do it, and what emerges in this (un)doing. In this reflexive and methodological paper, we engage with the affect and relationality of doing storywork. We reflect on and theorize around embeddedness, othering, belonging, power, shame, and joy in research encounters. Pragmatically, we consider how relational ethics combine with exhaustion and logistical challenges. Finally, we explore the tensions inherent to (co)producing stories at the boundaries of neoliberal academic temporalities and structures.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".