An investigation into the early stages of New Zealand's voluntary carbon market
Bibliographic record
Abstract
The voluntary carbon market (VCM) is a relatively mature field where institutions have become firmly established. This empirical paper explores findings obtained from interviewing key actors within the VCM in New Zealand, at a time when the organizational field was beginning to emerge (2010/2011). Fourteen semi-structured interviews were carried out with managers and decision-makers at 13 organizations, representing a cross-section of leaders in the field at the time. Participants were investigated regarding their cognitions as well as their operational activities and interactions with the range of actors in the field, and how these evolved over time. Case studies of the wine industry, the taxi industry and the carbon services industry are presented. Findings identify a number of early successes, including endeavors which focused on promoting market integrity through infrastructure and knowledge sharing amongst participants, which influenced others to shift behavior around climate change mitigation in general. Setbacks are evident as well, with the primary setback characterized as market stagnation: buyers and sellers drifting away from the market. Communication challenges, low certification recognition, risk of greenwash exposure, policy uncertainty, the global financial crisis and general disenchantment with the carbon market were listed as some of the causes.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".