Discrete Choice Modeling of Freight Outsourcing Decisions of Canadian Manufacturers
Bibliographic record
Abstract
Behavioral freight transportation modeling is an approach to enhance the quality of freight and logistics policy assessments. Outsourcing of freight activities is one of the essential decisions that firms make. Such decisions are influenced by factors such as economic conditions, competition, the industry, firm strategies, and firm characteristics. In this paper, the authors introduce a set of discrete choice models—binary and multinomial logit—that quantify the effect of some of these factors on outsourcing of the freight-related activities of goods production and logistics for Canadian manufacturers. The models were estimated with the use of data from the Survey of Innovation and Business Strategy obtained from Statistics Canada. The models consider firm characteristics (e.g., employment, number of products or services, and supplier locations), economic conditions, and local and international competition. The models also show the influence of the use of innovation and advanced technologies and measure the impact of government support programs for businesses (e.g., government grants) on outsourcing decisions. Models that explain international freight outsourcing are also presented. Model results highlight that freight outsourcing culture differs from one industry to another. Firms that use government training programs are more likely to outsource freight operations locally, whereas those that use government grants outsource their freight activities internationally. Model validation indicated acceptable predictive capabilities. Simplified models that were based on industry classification, location, and firm size were estimated and are to be used for future microsimulation purposes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".