Mobilizing transdisciplinary collaborations: collective reflections on <i>de</i>centering academia in knowledge production
Bibliographic record
Abstract
Non-technical summary Global sustainability challenges and their impact on society have been well-documented in recent years, such as more intense extreme weather events, environmental degradation, as well as ecosystem and biodiversity loss. These challenges require a united effort of scientists from multiple disciplines with stakeholders, including government, non-government organizations, corporate industry, and members of the general public, with the aim to generate integrated knowledge with real-world applicability. Yet, there continues to be challenges for these types of collaboration. In this commentary, we describe processes of collective un learning that serve to de center academia in collaborations leading to a more equitable positioning of practitioners engaged in collaborative global sustainability research.
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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 | 0.080 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.061 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.004 | 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".