Scaling up and scaling down supply chains in volatile resource-based economies
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
The growth of mobile workforces to support diversified resource extraction activities, compared to historically single-industry towns, represents a key change in rural and remote resource landscapes that has accelerated since the 1980s. Mobile workforces can present many opportunities to rural communities and economies. However, the capacity, viability and competitiveness of rural-based businesses to engage in supply chains serving mobile labour may be undermined by limited attention to how businesses manoeuvre downturns while maintaining a level of readiness to recover and scale-up in order to meet emerging mobile workforce needs. Drawing upon interviews with businesses in Fort St. John, British Columbia, Canada, our research uses the concept of resiliency to examine challenges and strategies associated with business capacity and agility to scale-up and scale-down in response to changing economic conditions associated with large-scale mobile workforces and related economic sectors. Our findings suggest that the capacity to scale-up and scale-down is shaped by capital, human resource and infrastructure strategies, inventory management and contract management strategies. Industry and state policies may also play a role supporting the conditions that will improve the agility, capacity and readiness of businesses operating in volatile resource-based economies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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