The Impact of Automation on Local Businesses: Economic Futures in Rural Canada
Bibliographic record
Abstract
Automated technologies, increased digitalization, and international events, such as COVID-19, are putting pressure on national and local economies to adapt or face rising unemployment and economic downturn. Rural communities are particularly impacted by these pressures as their economies are often built around a common industry, and the lack of access to reliable broadband creates significant barriers. This project asks: what are the implications of automation for labour in rural Canada? How prepared are rural businesses for change? What policy options are available to address the challenges resulting from the adoption of automated technologies? We conducted a localized assessment of how businesses in a rural community in Western Canada view the scale, nature and impacts of automation. Interviews with 60 businesses found that most business owners and managers are unprepared for technologically-driven disruptions. They are unlikely to adapt, adopt, or innovate in response to a changing business and economic environment. Recognizing that this compounds the employment and labour challenges already present in rural places, we present some policy options (such as re-skilling workers and closing economic gaps between urban and rural areas) that various levels of government and businesses could adopt moving forward.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".