Is Carrier Choice Different for 3PLs and other End-shippers? Some Preliminary Findings
Bibliographic record
Abstract
As companies have increasingly sought to outsource their non-essential activities, there has been a dramatic rise in the use of the services of external companies (often referred to as Third Party Logistics Companies or 3PLs) to organize transportation logistics. Little is known about the degree to which their choice of carriers differs from that of traditional end-shippers. Because this sector is expected to grow in the future and thereby to exert more influence on the way freight is shipped, understanding any differences that they might manifest in carrier choice is useful in itself, but also potentially critical in evaluating the potential for rail to increase its share of freight. This paper presents some findings of a unique shipper carrier-choice stated preference survey of shippers in the Quebec City-Windsor Corridor. The survey was conducted during the fall of 2005. The survey was designed explicitly to evaluate shipper preferences for the carriage of intercity consignments, and particularly their preferences for carriers that contract the services of rail companies to carry these shipments via trailer on flat car (TOFC). Preliminary analysis suggests that 3PLs behave differently from other end-shippers, and in particular, are even more mistrustful of the use of rail to move their consignments than other end shippers. Among other things, this suggests that increasing rails share of freight faces tremendous challenges.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".