Mixed Methods Research: A Comprehensive Approach for Study into the New Zealand Voluntary Carbon Market
Bibliographic record
Abstract
Climate change and solutions to solving this wicked problem require a mixed methods research approach that draws on quantitative and qualitative inquiry together. The purpose of this article is to demonstrate the applicability (and effectiveness) of a mixed methods approach applied to research into the voluntary carbon market (VCM), a key path available for organisations electing to offset their carbon emissions, in New Zealand. The mixed methods approach included three unique data sets (quantitative documents, quantitative surveys, qualitative in-depth interviews), and was both explanatory (qualitative interviews built upon and contextualized the document analysis and survey results) and convergent (data sets were examined separately, then, as they represent different aspects of the same phenomenon, were combined for analysis). These complementary methods were used to gain a fuller picture of the evolution and institutional dynamics of the VCM field in order to produce a comprehensive case study.
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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 | 0.197 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".