Climate Action Research: What’s Holding Us Back?
Bibliographic record
Abstract
Climate change is the grand challenge of our time (Sevil, Muñoz, & Godoy-Faúndez, 2022) yet, it has been observed that there is a dearth of management research on this issue (Nyberg & Wright, 2022). In this panel symposium, we go further than providing a simple ‘call to arms’ in respect of climate action research. We acknowledge that such calls are important in that they highlight the imperative to conduct research on this important theme, however they do not provide us with an explanation of why management researchers are not applying their research experience and expertise to it. Our symposium centers on a panel discussion where leading management researchers drawn from Europe, North America, and the Asia-Pacific, all of whom have engaged in climate action research, will debate why it is that management researchers are generally failing to engage with this critically important challenge. In doing so, they will address two fundamental questions: (i) ‘What’s holding us back from climate action research?’ (ii) ‘What key choices do management researchers have to make when deciding whether to undertake research on climate action?’ The outcomes of this discussion will, we believe, lower perceived barriers to research on climate risk among management scholars, guide future debate on the issue, and lead to research that positively contributes to climate action.
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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 | 0.148 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.032 | 0.065 |
| Scholarly communication | 0.056 | 0.076 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.051 | 0.065 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".