Public organizations as anchors and quartermasters of innovation : the case of ocean science instrumentalities in Nova Scotia, Canada
Bibliographic record
Abstract
Because there is a lack of empirical research on public innovation in goods, the importance of public organizations for innovation may be underestimated. I show that public organizations engage in the development of important and novel ocean science instrumentalities—instruments and techniques—in Nova Scotia, Canada. I conducted structured first-person interviews to collect data on 27 public and private organizations and the 702 possible interactive learning relationships between them. I use quantitative network analysis methods to confirm the importance of public organizations within this innovation system and to also investigate the nature of interorganizational interactive learning. I find that public organizations have greater degree centrality than private companies and that the removal of public organizations would result in greater network fragmentation than the removal of private companies. I also find that both public and private organizations perform more complex roles than suggested by the limited prior research on scientific instrumentality innovation. The majority of learning interactions between public research organizations and private companies in this network are symbiotic—multiplex and bidirectional. The most important relationships in the network involve bidirectional learning partnerships. These findings contradict the oversimplified view of innovation as linear market transactions. My work reinforces calls for goods to be included in studies of public innovation, makes several methodological contributions that can be used to reveal dark innovation, and identifies the anchoring and quartermastering roles that appear present in this particular scientific instrumentalities innovation system. I also highlight a potentially problematic disconnect between ocean science policies and ocean industry policies in this region.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".