Place-Based Organizing with Nature: The What, How, and Why of Place in Organization-Nature Relations
Bibliographic record
Abstract
Examples have shown that attention to place is essential for developing solutions to tackle social-ecological crises, such as climate change, biodiversity loss, and social conflict and inequalities. Organizing with nature, to integrate nature into organizational processes rather than to control, utilize, and exploit it as resources (e.g., Dentoni, 2024; Ergene, Banerjee, & Hoffman, 2021; Howard-Grenville & Lahneman, 2021), is thus not universal but place-based and requires organizations to have ecological knowledge of a place (Kourula, Georgallis, Henriques, & Mair, 2024; Rahman, Nguyen, & Slawinski, 2024). Place, a multidimensional concept, refers to “a built or natural landscape, possessing a unique geographical location, invested with meaning” (Shrivastava & Kennelly, 2013: 84) and is “shaped by, and further shape(s), people’s everyday social life and interactions” (Dacin, Zilber, Cartel, & Kibler, 2024: 1192). From the lens of organization-nature relations, a place is material, already organized with nature, and continuously constituted through actors, both human and other-than-human, interacting with one another through time. Despite some scholarly attempts in this direction, there remains a lack of understanding of how organizations can better acknowledge their deep entanglement with nature in a place and identify place- based approaches toward sustainable organizing in their local environment (Kourula et al., 2024; Shrivastava & Kennelly, 2013). With the occasion of the Academy of Management Annual Meeting 2025 to be outside of North America and in Copenhagen for the first time, this panel symposium aims to draw our scholarly attention toward place- based organizing with nature. The symposium invites a joint conversation between the panelists and audience to collectively do phenomenon-based theorizing that “leverages real-world phenomena as inspiration or motivation for theory development or refinement” (Fisher, Mayer, & Morris, 2021: 632), which can be then “applied in specific contexts of practice” through engaged scholarship (Van De Ven & Johnson, 2006: 803). Together, we will critically examine the what, how, and why of place as organizations incorporate nature into their organizational processes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".