Industry-science collaboration in shellfish aquaculture and the management of knowledge processes
Bibliographic record
Abstract
In the new global, knowledge-based economy, knowledge is recognized as a driving force of social and economic development: it is the key to innovation, as well as to the goal of sustainability. Growing economic, social, political and environmental pressures on both the industry and the science sector have driven many organizations to re-assess their own capacities of producing, mobilizing and absorbing knowledge, and search for effective knowledge management strategies. One strategy that is increasingly utilized is the process of intersectoral collaboration. Collaboration between the two sectors of industry and science has been particularly fostered by government, through R&D policies and funding strategies. It is seen as an efficient strategy to improve knowledge production, diffusion and absorption capacities across both sectors, by creating synergies. However, industry and science operate within different contexts. Collaboration between them often presents significant difficulties. Using the case of shellfish aquaculture in Canada, this exploratory study takes a broad sociological approach in the investigation of industry-science collaboration. It explores mainly the phenomena of occupational cultures and knowledge networks, in order to seek a better understanding of some of the social processes by which shellfish aquaculture knowledge is produced, diffused and validated. The study uses qualitative methods and interviews with shellfish growers and aquaculture scientists in three different regions of Canada, in an attempt to identify some of the structural, cultural and relational factors that may affect collaboration processes between them. Once we have identified and understood the factors that favour or inhibit intersectoral collaboration, we may be in a better position to develop improved tools and mechanisms that will facilitate the process and allow both the industry and the science sector to achieve the full benefits of the knowledge that is being developed.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.030 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".