Consumption to compensate for the feeling of “loss of ownership of self” Women’s journeys through the liminal transitions of marriage
Bibliographic record
Abstract
Abstract This interpretive study derives a conceptual framework explaining how Indian women experience a loss of self through the transformational event of marriage. The self‐discrepancy of losing one’s self motivates these women to renegotiate their sense of self through consumption. Data were collected through 76 in‐depth interviews analyzed by a constant comparative method and grounded theory. Our research question is to what degree and in what ways do women in India experience a loss of self upon marriage and, if they do, through what consumer practices (if any) do they attempt to compensate for this feeling of loss? Analysis revealed three common stages in Indian women’s married life (1) marriage as a life transitional event inducing liminality (2) loss of Self: dissonance (3) compensatory consumption as a response to dissonance. Our research contributes to life cycle theory by focusing on liminal transitions. We find that self is a possession that women can lose after marriage in India. Based on these findings we argue that understandings of identity and life cycle must be amended to consider self‐discrepancy and coping processes. We develop a model of consumption that results from a perceived loss of self. This is all part of a process of self‐identity reconstruction. We also expand psychological ownership theory to include ownership of self.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".