Meri Kahanee Sono (Listen to My Story): A (Step) Mother's Journey Of Healing and Renewal
Bibliographic record
Abstract
Loyalty conflicts. Resistance. Anger. This thesis will take you along on my journey as a South Asian woman and the mother and stepmother of a cross-cultural stepfamily. Through the form of an arts-informed auto-ethnography I will illustrate how I underwent personal and spiritual transformation while (step) mothering four children. It is a story that “both cuts and heals” (Luciani, 2000, p. 39). In this work I show how mothering and stepmothering can “deteriorate into martyrdom if a mother gives her children and spouse the love and care she doesn’t feel that she herself is worthy of receiving” (Northrup, 2005, p. 13). I explore how the pressure to be a “good mother” and “good stepmother” left me feeling inadequate, resentful, doubtful of my abilities and neglectful of my own needs. Hope. Solace. Spirituality. Love. This story is also about healing and renewal and my process of recapturing a sense of self by returning to spirituality. By sinking into my life as a mother and stepmother and viewing my life circumstance as a “vehicle for waking up” (Chodron, 1991, p. 71), I cultivated a conscious state in which anger and resentment was replaced by awe and wonder. I strengthened my agency by directing nurturing and caregiving to myself, pursuing my creativity, and sharing childrearing more equitably with my partner. Mothering and stepmothering became sites of empowerment as I found joy in my relationship with myself, my children, and the community around me. This research provides an example of how meaningful knowledge production can occur in alternative forms to mainstream academic discourse. Arts-informed, auto-ethnographic research offers insights on human relationships and interactions in the world by fostering an epistemological shift for the researcher as well as the reader. As Sameshina and Knowles note (2008) this methodology is “transformational in process and possibilities” (108).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".