Intensive mothering and informational habitus: Interplays in virtual communities
Bibliographic record
Abstract
Abstract Objective This study aimed to explore how virtual communities of mothers shape the informational habitus in the context of the intensive mothering ideology. Background Mothers' involvement and dedication are perceived as essential to children's development. Some mothers join virtual communities for health information to ensure that they are doing the best for their child. Method An online ethnography in three virtual communities of mothers was conducted, in addition to individual interviews with 16 mothers of young children (18 months and below). Results Mothers use virtual communities for emotional and informational support. Experiential knowledge and referenced information are highly valuable. Furthermore, mothers are reflexive and choose what information they integrate based on their educational capital and their personal skills. Finally, choice appeared to be the practical operator of the informational habitus. Conclusion Our results suggest that the sense of belonging developed in virtual communities shapes a new informational habitus based on the importance of being an informed mother. Implications Mothers want to make the best possible decisions for their child's health. To do so, they deploy reflexive practices to process information. This suggests that other than their physicians, they also trust an important array of sources of information.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".