Bibliographic record
Abstract
Environmental leaders have a huge impact on critical environmental issues. We sought to learn about their values, leadership philosophies, and leadership-related challenges. We compared environmental leaders with leaders of other kinds of organizations, as well as comparing leaders of for-profit and nonprofit environmental organizations, using qualitative and quantitative research traditions. In this chapter, we explain why and how we chose multiple research methods, and discuss the approaches and findings to illustrate how an open-ended approach enhanced our findings. Rather than viewing various research methods as part of an incompatible qualitative–quantitative dichotomy, we approached them as complementary modes of investigation, resulting in a deeper understanding of the environmental leadership phenomenon. One of our challenges was determining how to analyze and integrate our data, since our method generated an unwieldy amount, and surfaced unexpected findings. An equally important facet of our research was the interpersonal. One rewarding aspect was the rapport and empathy we developed with our respondents, which resulted in findings that would be immediately useful to practitioners, not simply to theoreticians. Ultimately, the success of our research depended on our own relationship. The project proved to be far more than an intellectual exercise, but involved the interaction of our emotions and personal values, as well as the social climate in which we conducted the 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 | 0.086 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.092 | 0.019 |
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".