‘Putting all my eggs into the app’: Self, relational and systemic surveillance of mothers’ use of digital technologies during the transition to parenting
Bibliographic record
Abstract
This paper builds on thematic findings from a larger study that explored how digital technologies (e.g. smartphones, apps, search engines) shape expectant and new mothers' early parenting practices. An overarching theme that arose across these mothers' experiences which deserved deeper exploration was relational digital surveillance. In the context of this paper, relational digital surveillance describes how mothers evaluate their sense of preparedness, goodness or suitability for motherhood as they transition into parenting in relation to: their own use of digital technologies when caring for their pregnant bodies (self-surveillance), partners' and family members' commentary and/or judgement regarding their use of digital technologies to support their parenting and decision-making (familial surveillance) in addition to service/health care providers' commentary and/or judgement concerning their technology use (systemic surveillance). Mothers' use of digital technologies in this study not only provided others (partners, family members, health care providers) with means to watch over their actions and bodies as they transitioned into motherhood but offered a new evaluative dimension for others to scrutinize their behaviour as a new mother. Such understandings of relational digital surveillance within the transition to parenting context raise critical questions concerning the promotion and commercialization of digital self-surveillance technologies among expectant/new parents given the ways these technologies can further push the boundaries of hegemonic mothering practices and contribute to feelings of inadequacy and self-doubt. Alternatively, these insights offer avenues where health care providers can intervene to facilitate activities that enhance digital health literacy skills and mitigate parents' exposure to platforms that amplify anxieties.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".