Sustainable Innovation Postpandemic: Perspectives From the U.S., Canada, Norway, and Thailand
Bibliographic record
Abstract
This paper explores perspectives for improved sustainable innovation for the post COVID-19 pandemic era. We conducted online interviews between October and December 2020 with 16 individuals working in social entrepreneurship, tech startups, established companies, and non-profit business support organizations across the US, Canada, Norway, and Thailand. Our findings reveal a shared commitment to four themes related to sustainable innovation post-pandemic: (1) thinking of local problems as global problems, in the same way that innovation technologies and businesses can cause global disruptions; (2) including historically underserved populations in innovation business activities, as it is also important to truly understand how technologies and innovation businesses impact the lives of people experiencing low-socioeconomic status; (3) helping people in remote communities, low- and middle-income countries, and wartorn areas is essential – offering hands-on basic computing training could be an option for helping people gain employment in the digital economy; and (4) having governments lead postpandemic sustainable innovation projects and then invite businesses to participate. In addition, this paper proposes a theoretical framework for fostering innovation in resourceconstrained environments following large-scale disruptions.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".