From network to meshwork: Becoming attuned to difference in transdisciplinary environmental research encounters
Bibliographic record
Abstract
Transdisciplinary research has been promoted as a means of bringing together certified experts and stakeholders to produce knowledge that is policy-relevant, salient, credible, and legitimate to inform decision-making about complex problems. In this article I discuss the limitations of using the ‘network’ metaphor in transdisciplinary research practice and propose the use of a different metaphor to make transdisciplinary research encounters more attuned to difference. This research is informed by Tim Ingold’s use of ‘meshwork’ as a metaphor for how life is lived along lines of becoming: emergent, indeterminate, contingent, historical, narrative. In this paper, my objective is to explain and illustrate by way of an example of a transdisciplinary climate change adaptation project the need for a new metaphor to convey the open-endedness of transdisciplinary research where subject positions are not conceived in advance of a research encounter, such as in the ‘network’ metaphor, but erupt in the interstices of research methods, objectives and desired outcomes. The meshwork metaphor implies that transdisciplinarity should be reframed as a practice of attunement to difference, becoming skilled in paying attention, witnessing, and responding to differences.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.088 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.007 | 0.009 |
| 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".