Resilient by Design: Enabling Agility and Resilience in Ontario’s Small and Medium Enterprises
Bibliographic record
Abstract
At the time of writing this report, Canada has completed one year of lockdowns and restrictions due to COVID-19. The world is grappling with disruption at a scale that we haven’t experienced in recent years. The devastation COVID has wreaked through the Small and Medium Enterprise (SME) community is just starting to show and it is expected to get worse. In 2020, Canada lost almost 58,000 businesses and by Canadian Federation of Independent Business’ (CFIB’s) estimates, Canada could lose between 71,000 to 222,000 businesses which equate to 7% to 21%, respectively. With up to almost 3 million jobs at stake, it is not hard to imagine the devastation that this could unleash. SMEs make up almost the entirety of Canada’s economy, with 98.8% of businesses in Canada being organizations with 1-499 employees. Without question, where there is impact to Canadian SMEs, there is impact to Canada’s well-being. Agility and resilience offer SMEs a way forward. This study looks to answer the main research question of: what are the elements of agility and resilience and how might they enable us to implement resilience in Ontario SMEs? In answering this question, this study explores the relationship between the two concepts and adds in an original contribution that enumerates the dimensions of resilience that can be used to evaluate resilience at the time of impact. In addition, original contributions of this study also include seven elements of agility and resilience and an accompanying Agility and Resilience Maturity Model that can enable SMEs to not only identify their current-state resilience but also to have a roadmap of transformation for resilience.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".