Organizing for the Climate Emergency: Organizational Underpinnings of Climate Action
Bibliographic record
Abstract
In 2019, the Oxford Word of the Year was climate emergency, “a situation in which urgent action is required to reduce or halt climate change and avoid potentially irreversible environmental damage resulting from it.” Indeed, with other developments such as the rise of the Fridays for Future school strikes for the climate and the naming of 16-year old Greta Thunberg as TIME Person of the Year, it is no stretch to say that it was a monumental year for climate change. Sadly, while the science behind climate change is clear and tens of thousands of scientists have declared a climate emergency that could bring catastrophic effects for humanity, society continues to face grave challenges of inaction on the parts of individuals, organizations, and entire nations. To reflect on and discuss how we can redress shortcomings of climate action and contribute to tackling the climate emergency, we have organized a panel symposium featuring leading organizational theorists researching climate-related issues. Overall, this symposium seeks to bring together scholars to explore and discuss promising avenues for research on the specific challenge of climate change within the domain of organizational and management research, as well as how we can, practically speaking, conduct such research in a way that positively impacts both theory and practice. Key topics and questions concerning climate change and action to be explored in this symposium include: a.) clarifying what system change means and entails, b.) constructing a commons logic and collective action frames, c.) overcoming polarization and the role of emotional responses, d.) winding down traditional businesses and identifying transition pathways, and e.) producing actionable knowledge.
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.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".