Logistics sprawl in North America: methodological issues and a case study in Toronto
Bibliographic record
Abstract
This paper focuses on the spatial patterns of freight and logistics activities in North America. The recent interest in logistics and warehousing and its impact on the urban environment has prompted research investigating the 'sprawling' nature of these firms. Logistics sprawl, i.e. the spatial deconcentration of logistics facilities and distribution centers in metropolitan areas has been examined for several metropolitan areas (Dablanc and Ross 2012; Dablanc 2014; Dablanc et al., 2014), yielding contrasting results: Atlanta and Los Angeles have experienced strong logistics sprawl between 1998 and 2008 while Seattle has not. The objective in this paper is two-fold. An additional case study (Toronto) is investigated to expand the current understanding of North American logistics sprawl and methodological issues, particularly related to facility identification and location data are discussed. An updated method for analyzing spatial patterns of logistics activity in North American cities is subsequently proposed. This updated method may then be used in the future to re-examine former case studies (Los Angeles, Atlanta, Seattle) as well as to investigate new ones.
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 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".