Towards Innovation (Eco)Systems: Enhancing the Public Value of Scientific Research in the Canadian Arctic
Bibliographic record
Abstract
Over the past decade, the Canadian Arctic has seen an intensification of scientific research designed to foster innovation (i.e., the process of transforming ideas into new products, services, practices or policies). However, innovation remains generally low. This paper argues that before we can meaningfully promote innovation in the Arctic, there is a need to first identify the complex systems that support or inhibit innovation. Few, if any studies have taken a systems approach to enrich our understanding of how existing networks may or may not support innovation in the Canadian Arctic. A promising, but under-explored approach is to consider innovation ecosystems, defined as the multi-level, multi-modal, multi-nodal and multi-agent system of systems that shape the way that societies generate, exchange, and use knowledge. This paper presents innovation (eco)systems as a potentially valuable systems-based approach for policy actors to enhance innovation linkages in the Arctic. From a policy perspective, there is a need to embrace and promote more networked approaches to co-create public value and to consider the lifespan of any innovation. Potential directions for future research include: mapping the actors involved in Arctic innovation ecosystems (including intermediaries and bridging agents) at multiple scales; the role that formal and informal institutions play in shaping co-innovation; case studies to evaluate innovation processes; and an assessment of the coupled functional-structural aspects that influence innovation outcomes in the Canadian Arctic.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".