Mixed Methods Research: A Comprehensive Approach for Study into the New Zealand Voluntary Carbon Market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it