Collective Action for a Multispecies World: A Compositionist Approach to Grand Challenges
Bibliographic record
Abstract
As the field of management studies widens its scale of reflection to consider the socio-ecological ecosystems of which organizations are part, more attention is devoted to grand challenges. While extent literature generally treats them as exogenous objects, our focus here is on unfolding encounters with grand challenges. We conceive grand challenges as concrete problems of arbitration of more-than-human ways of life, where the managerial practices of organizations enact and transform grand challenges. We put forward a posthumanism and pragmatist style of thinking, which, we argue, can help us think with grand challenges and engage in creative ways of composing a common world. Through the story of a problematic situation where tangles of grand challenges abound, we offer a mode of construction that can help us compose what is, in a given situation, a ‘better’ world. This mode of construction is based on three sets of practices, namely, slowing down, multispecies world-making, and being present and grieving losses. It facilitates the emergence of new ways of composing the world, helps account for the implication of other species, and foregrounds the elaboration of worlds in a response-able way. Our paper contributes to the grand challenges literature by proposing a mode of attention and action that engages both management researchers and practitioners in the work of constructing multispecies worlds.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| 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".