Local Content and Mobile Labour: The Role of Senior Governments in Capturing Benefits for Local Communities
Bibliographic record
Abstract
Rural resource-based regions are increasingly accommodating large mobile workforces to support renewed industrial investments. As large-scale resource development projects are mobilized, however, industries’ use of mobile work camps and outsourcing with established global supply chain networks may exclude rural businesses and communities from associated economic benefits, particularly in the absence of local content policies. To date, however, research has largely focused on supply chain opportunities pertaining to the construction or operations of resource development assets, with limited consideration of the issues that must be considered in order for local businesses to capture benefits from mobile workforces. Drawing upon interviews with businesses in Fort St. John, British Columbia, Canada, we use issues unfolding in the new institutionalism discourse to explore the roles of senior governments in strengthening local benefits related to mobile workforces, as well as some of the challenges that existing policy roles and debates present to better position rural businesses in these resource-based regions. Our findings suggest that underdeveloped and under-resourced senior government policies, regulations, and processes are entrenching the role of rural regions as resource banks instead of creating a competitive playing field for rural businesses to capture, and locally anchor, economic benefits. Keywords: local content, rural businesses, mobile workforce
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".