Bibliographic record
Abstract
There is consensus in the field of sustainability science that co-production of knowledge is needed to generate knowledge that is useful for addressing matters of concern. The field has made important advances, particularly focusing on developing strategies and principles that ensure the effective co-production of knowledge. Although these lay necessary foundations, less attention has been paid to the question as to what exactly is meant to “happen” in such processes. What is meant to happen, we argue, is that such processes generate knowledge that affects, by which we mean that it triggers an experiential intensity. Although affect ultimately underlies all kinds of knowledge (e.g., representational, such as discourse, or embodied, such as habits), different kinds and contents of knowledge affect (or not) participants of co-production processes in different ways. This paper thus argues that paying attention to affect in knowledge co-production increases the likelihood that it will be acted upon. To illustrate this point, we conceptualize knowledge as an assemblage generated through processes of knowledge co-production. We argue that for knowledge to affect, it must align the different kinds of knowledge mobilized in the process with the concrete experiences of those meant to act on it. In this paper, we particularly focus on the representational, discursive kind of knowledge, often of scientific nature, which continues to dominate processes of knowledge co-production, and explore alignment dynamics with the affective. In particular, we argue that applying methods and techniques that give room to the multimodal and multisensory nature of affect in co-production processes can support such alignment. We argue that the picture of knowledge co-production that emerges from our work as a potentially open-ended process of assembling is adequate for engaging with complex sustainability concerns in a world in constant becoming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".