Towards an incoherent convergence science: diverse economies, crises, and recoveries, and the hope for better futures
Bibliographic record
Abstract
Here, we argue for a critical approach to convergence science: one that develops collaborative problem-solving for pressing contemporary crises. We ask for researchers to encounter spaces where diverse epistemological and ontological perspectives can build solutions based on on-the-ground practices and existing knowledge. This approach contrasts with status quo crisis responses, which are imbricated with dominant forms of capitalism, and whose solutions reinforce the very systems that caused these crises. An incoherent convergence, in contrast, requires university researchers to come together with other knowledge bearers to lay bare the incongruities among systems while also encouraging ontological and epistemological pluriversality without assuming a singular understanding, a singular path forward, or a shared worldview. We draw on the situation in Mora, New Mexico, USA, and its recovery from the Hermits Peak Calf Canyon wildfire of 2022 to illustrate the disjuncture that arose between the community and the dominant disaster response regime. We argue that convergence science has the potential to address such failures, but only by embracing rather than rationalizing the messiness of on-the-ground realities. Without a new approach, applied research may continue to reproduce the structural inequalities among these diverse communities, including the political-economic processes wrought from climate change. Convergence science, we argue, needs spaces of engagement with that which remains illegible within the privileged scientific paradigm.
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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 | 0.072 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.021 | 0.132 |
| Scholarly communication | 0.032 | 0.070 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".