Proximity, land, labor and planning? Logistics industry perspectives on facility location
Bibliographic record
Abstract
Distribution, warehousing and logistics facilities located in Canadian municipalities have significant impacts on surrounding land uses and on nearby transportation infrastructure, not to mention the broader socio-economic environment. While there is considerable literature available concerning the location choices of generic industrial firms, explorations of logistics firms' locations have been less extensive. This is somewhat surprising because of the increasing ability of logistics firms to relocate and the potential issues surrounding their activity, for example related to the amount of freight traffic that they generate. The goal of this research is to explore the relative importance of location factors that attract/retain logistics firms to a community, and identify potential issues of operational conflict between municipalities, their residents, and logistics firms. Factors that were found to negatively influence the attractiveness of a particular location (push factors) were land costs and tax rates, a lack of skilled workers, and a lack of land available for expansion on site. Location factors that retained firms at a particular location were access to customers and suppliers, having the ability to operate 24/7 and proximity to highways. There is a need for the public sector, including planners and economic developers, to better understand the requirements of the logistics industry in order to accommodate these firms while mitigating potentially adverse impacts to communities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".