Intergenerational Voices of Adoption: Family Stories of Adoptees and Their Adult Children
Bibliographic record
Abstract
Little is known about adoptees’ experiences through adulthood. Individuals sometimes make sense of their heritage, and come to understand and express themselves in their unique voices, through storytelling. Five mother-child(ren) dyads and triads participated. The mothers had been adopted in infancy and raised by an adoptive family. Their biological child(ren) had grown up aware their mother was an adoptee. The feminist relational method of the listening guide (Gilligan et al., 2003 Gilligan, C., Spencer, R., Weinberg, M. K., & Bertsch, T. (2003). On the listening guide: A voice-centered relational model. In P. M. Camic, J. E. Rhodes, & L. Yardley (Eds.), Qualitative research in psychology: Expanding perspectives in methodology and design (pp. 157–172). American Psychological Association.[Crossref] , [Google Scholar]) was employed to hear the adoption stories passed from one generation to the next. Parent and adult child(ren) were interviewed together to explore voices embedded in the relational context of their story. The joint family interviews were analyzed for these different voices. The findings suggest mothers passed down stories of positive adoption experiences through voices that were both embracing of their adoption narrative and sometimes cautious. The children spoke from voices of embrace and curiosity. Both generations stated the meaningfulness of co-constructing family adoption narratives.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".