Actions towards the joint production of knowledge: the risk of salmon aquaculture on American Lobster
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Joint production of knowledge (JPK) is said to facilitate proactive mitigation of risks in marine resource management. However, lack of consensus on who should be involved, when it is happening and the exact mechanisms of sharing knowledge has precluded the development of an effective implementation framework. Here, we explore one approach to building a post-normal science, one that both includes local ecological knowledge and bridges scientific silos. We first identify several actions of knowledge production and then provide an Atlantic Canadian case study, drawn from an assessment of the impact of aquaculture on American lobster, to illustrate necessary actions on the road to JPK. Key actions include theorizing relationships, agreeing on key concepts, specifying, and interpreting required data, identifying principles and making evaluations. We fill a lacuna in the JPK literature by: first, defining knowledge as the result of a set of actions; second, using knowledge generating actions to explore how different knowledge sets come together to contribute to JPK; and third, identifying how knowledge actions can facilitate or inhibit JPK. We conclude that this list of the essential actions of knowledge production is necessary to the successful development of alternative approaches to risk.
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.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it