Work-readiness discourse : indigenous perspectives on work, and the limitations of Australian indigenous employment policies
Bibliographic record
Abstract
High rates of unemployment amongst Indigenous Australians in comparison to non-Indigenous Australians have been rendered a public policy problem by successive Australian Governments. The solutions are often coercive forms of neoliberal governance. Despite policy interventions targeting such populations in urban areas with an availability of jobs, unemployment rates remain higher than for the non-Indigenous population. This thesis introduces a new conceptualisation of policy and governance limitations and social action to contribute to understandings of unsuccessful Indigenous employment policy outcomes. I utilise the concept of metis knowledge – a form of know-how that originates from contextualised, practical experience – to critique policy and illustrate its role in limiting the aims of governance. Indigenous employment policy that governs through pedagogical technologies applied to the Indigenous workforce demonstrates this limitation through its assumptions that the metis knowledge required to become ‘work-ready’ can be transferred unproblematically. Where Indigenous people are driven by different motivations, ideas and aspirations in relation to work, Indigenous employment policies face the issue of epistemological dissonance. That is, the transferability of metis knowledge places a limit on the ability of Indigenous employment policy’s aim to create ‘work-ready’ subjects. I combine a critical and comparative methodological approach to analysing both Indigenous employment policy documents and interviews conducted with Indigenous respondents. I apply a critical discourse analysis to Indigenous employment policy documents to reveal an underlying work-readiness discourse and to highlight its effects and assumptions. I also thematically analyse interview material to uncover Indigenous respondents’ metis knowledge and their reactions to the imposition of ‘work-readiness metis knowledge’. A key finding arising from my comparison of these two datasets is that respondents experienced feelings of shame and ‘shamejob’ in relation to mainstream work and workplaces. That is, Indigenous employment policy creates ‘institutionally generated shamejob’. The clash between two sets of metis knowledges caused many to feel discouraged and demotivated in relation to participating in or finding work. I discuss these findings and their implications for policy and governance theory. In particular, there is a need for policy to recognise diverse orientations to work, and for a more epistemologically oriented view of how governance in plural liberal democratic societies functions or not.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.047 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".