Culture Counts: A choice modelling approach to quantifying cultural values for First Nations peoples
Bibliographic record
Abstract
In response to the research question 'What methods are feasible, reliable and appropriate for quantifying cultural values for First Nations people?', this thesis demonstrates how choice modelling can elucidate and measure the value that Indigenous people place on aspects of their culture by privileging their viewpoints. Measuring and incorporating these intangible values in policy and legal frameworks is essential to effectively and substantially give voice to First Nations peoples. The idea behind this research developed while travelling with a Nyikina elder who spoke of the tension between income from employment establishing a livelihood in the western way and the cultural activity essential for maintaining cultural connection, identity and wellbeing. Using those tensions and trade-offs in choice modelling enables measurement of intangible values of culture. Choice modelling, which includes discrete choice experiments and best-worst scaling, has strong theoretical bases in economics and psychology, supported with rigorous mathematical architecture. It has been shown to accurately predict actual behaviour, with fewer behavioural biases than contingent valuation. Limited application of choice modelling with First Nations people has occurred in cultural heritage, environmental and resource management contexts. Multiphase fieldwork in several west Kimberley locations, with Nyikina and Mangala people, started with qualitative research to build trust and inform the selection of attributes and levels for the choice model. In order to yield maximum information about preferences, with minimum cognitive load, a Best-Worst Scaling Profile Case model with a supplementary question about profile acceptability, a discrete choice experiment, was developed and incorporated into a survey. The relative preferences identified in responses yield dollar valuations for the cultural attributes. Overall, access to traditional Country had the strongest responses to lowest and highest levels yielding marginal values of up to {dollar}0.44 million per person per annum. As well as informing methodological development, this research has significant implications and potential applications in policy and legal contexts. The need for Indigenous viewpoints to be central in policy development is generally accepted, and choice modelling could support valuation in this context. Failures to protect cultural heritage have highlighted the need to quantify and incorporate cultural values in decision making, rather than dismissing them as immeasurable. Application of choice modelling in native title compensation cases could overcome the current limitation of determinations being reliant on judicial intuition and market proxies.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".