A systematic literature review of non-market valuation of Indigenous peoples’ values: Current knowledge, best-practice and framing questions for future research
Bibliographic record
Abstract
Non-market valuation (NMV) can be effective to understand the value people place on ecosystem goods and services for which there are no market prices. Over the last 20 years, NMV has increasingly been applied to Indigenous contexts, albeit with important conceptual and methodological limitations. We conduct a global systematic literature review and detailed meta-synthesis of 63 peer-reviewed studies on NMV research applied to Indigenous peoples’ values. Selected studies are categorized by methods, year of publication, geographic area and ecosystem components. Australia (n = 19), the USA (n = 9) and Canada (n = 8) account for over half of all articles. Important knowledge gaps remain in the NMV peer–reviewed literature for other geographic areas. Our taxonomy based on ‘whose values’ and ‘which values’ reveals that a large proportion of studies (n = 24) focused on values held by Indigenous peoples, predominately on direct-use values (n = 12) and total economic values (n = 10). Studies based on the general population (n = 17) typically examined altruistic and/or existence values (n = 15). Our analysis identified seven main strategies used by previous studies to overcome critical limitations of NMV when applied to Indigenous peoples’ values. Strategies include: (1) engaging directly and ethically with Indigenous peoples; (2) investigating multi-dimensional values; (3) valuing health benefits; (4) adopting non-monetary payment vehicles; (5) using market prices for valuation; (6) sampling the broad population; and (7) investigating non-cumulative values. Based on this review, we provide seven critical questions to guide future NMV research: (1) What is the purpose?; (2) How does Indigenous knowledge inform NMV?; (3) Who benefits?, (4) What ethical frameworks apply?; (5) Whose values are considered?; (6) What is the expected change?; and (7) How are NMV limitations handled? Our contribution provides researchers and policy-makers with the most up-to-date review of the state-of-knowledge and suggestions for best-practice on the use of NMV methods when applied to Indigenous peoples’ values.
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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 | 0.072 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".