Towards Innovation (Eco)Systems: Enhancing the Public Value of Scientific Research in the Canadian Arctic
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
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.
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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 it