The Best Laid Plans: Why New Parents Fail to Habituate Practices
Bibliographic record
Abstract
Abstract Consumers regularly fail to habituate newly adopted practices. In contrast to established practices, this often occurs because understanding a practice is different from actually doing it. Our work explores this “messiness of doing” and explains why consumers successfully habituate some newly adopted practices after experiencing obstacles (i.e., misaligned practice elements) but not others. Utilizing a longitudinal approach that follows first-time parents from pregnancy through the first eight months postpartum, we track how parents plan for practices and how those plans unfold. We document a process whereby parents first engage in extensive planning and preparation prior to the birth of their child, during which parents build two realignment capabilities (anticipation and integration). After the baby’s arrival, some practices invariably do not work. Parents respond to these misalignments by following one of five paths—differentiated by the capabilities parents build while planning—that result in practice abandonment, vulnerable habituation, or habituation. Our work highlights the challenges associated with translating a social practice into an enacted practice and the corresponding importance of accumulating realignment capabilities during planning. To facilitate habituation of newly adopted practices, how consumers make plans for these practices may ultimately matter more than what they actually plan to do.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".