The best of intentions: mainstreaming, the not-for-profit sector and Indigeneous Australians
Bibliographic record
Abstract
This study investigates interconnections between government approaches to policy in Indigenous affairs – characterised by mainstreaming of services for Indigenous Australians – and the ways in which the not-for-profit sector (NFP) has responded. In terms of both policy and practice it offers a window on the intercultural and interpersonal challenges for organisations and individuals working in the cross-cultural spaces evolving between mainstream (white) organisations and Indigenous Australians. The thesis offers a detailed case study of Australian Red Cross – one of Australia’s oldest and most prestigious humanitarian organisations. In 2007, Red Cross commenced new programs and services for Indigenous Australians as part of its mission “to help the most vulnerable”. Drawing on Nakata’s concept of the “cultural interface” and field-based research across Australian Red Cross, the thesis explores the interfaces between Indigenous staff, the organisation, and Indigenous communities in the early stages of this venture during the period 2010-2012. The thesis also reviews in detail the experience and challenges of adapting and introducing a Canadian family/community safety program to Australia as an Indigenous community development program. As NFPs move into domains that were previously mainly Indigenous and with increased co-dependence between the NFP sector and government in providing Indigenous programs and services, the thesis offers a timely account of lessons, risks and challenges for all involved. In conclusion, the thesis questions whether the current policy direction and its resulting collaboration between governments and the mainstream NFP sector have secured the outcomes intended.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".