Cross-cultural adoptions and their effect on identity formation
Bibliographic record
Abstract
Adoption is a custom that has existed for many generations.However aspects of this practice have been, and continue to be controversial.This project examines issues relating to Aboriginal cross-cultural adoptions and their effect on identity formation.It asks the question "Does cross-cultural adoption negatively effect identity formation in Aboriginal children?"This question is not answered directly but insight is provided into the disputes and problems that arise in the context of cross-cultural adoption.While this project is grounded in research relating to identity formation, cultural identity and cross-cultural adoptions, it also contains a historical overview with a focus on Aboriginal children growing up in caucasian homes.The aim of this project is to gain a better understanding of how best to meet the needs of Aboriginal children growing up outside their birth families and communities.A framework for the issue of Aboriginal cross-cultural adoptions is provided through an instrumental case study of one woman's struggle to find her Aboriginal identity growing up in a caucasian home.This story, based on a case example from the writer's own practice experience, highlights past practice approaches and the need to change policy to reflect the best interests of the child.Finally, implications for policy are examined with the hope that these suggestions may provide children and families impacted by cross-cultural adoptions with the support they need to develop confident and secure identities to live in a society where they will inevitably experience racism and prejudice.when adopted into non-Aboriginal homes.I strongly believe that while every child deserves a family it is vital that we consider the effects these adoptions have on Aboriginal children and their communities.I hope that this project will increase awareness around the importance of a child's unique cultural identity and the maintenance of familial ties.I would like to take this opportunity to offer my sincere thanks to Professor Glen Schmidt, my Masters of Social Work Project Supervisor.He has been invaluable as a supervisor and teacher and I am very grateful for his help and encouragement.Thank you also to Margo Greenwood and Sandra Kioke for the time and energy they invested into my education.It was very much
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".