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Record W7027046729

Boosting Rail Competition

2022· article· en· W7027046729 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainCompetition (biology)Boosting (machine learning)IncentiveService (business)Demand patterns
DOInot available

Abstract

fetched live from OpenAlex

People familiar with the modern freight rail network in the United States know that regulation, not competition, is the invisible hand that guides the industry. This reality impacts everyone, from manufacturers to consumers, through delayed shipments and higher prices. Lack of rail competition exacerbates U.S. inflation and supply chain crises because railroads can, in effect, charge whatever they want for whatever delivery timeframe best suits them. In the end, consumers get products slower and at a higher cost. The rail industry’s ongoing problems are a direct result of decisions made by the railroads themselves. They implemented massive job cuts during the COVID-19 pandemic, which left them unprepared for the uptick in demand as the U.S. economy began to return to normal. Moreover, the railroads were incredibly slow to react to this problem, and their job numbers continue to leave them understaffed to provide adequate service. Equally problematic has been the unrelenting decline in service provided by railroads in recent years. This problem has been on full display amid supply chain issues felt by consumers across the U.S. landscape. Unilateral actions by several railroads to curtail shipments of fertilizer inputs and grains could cause major supply chain disruptions, hurt American farmers, and worsen the food crisis. A bipartisan group of 51 members of Congress recently wrote to the U.S. Surface Transportation Board (STB) urging it to “ensure critical commodities reach essential industries and workers, such as America’s farmers, who are essential to feeding our nation and the world.” After all, as the legislators noted, “food is a national security issue, and we must treat it as such.” As recently as 1980, more than two dozen major railroads competed with one another. Over the years, mergers and acquisitions have led to an industry comprising just four mega-railroads running de facto monopolies across broad swaths of the United States. Shippers that must rely on one of these railroads to move their products are rightfully known as “captive” shippers. According to data from the Rail Customer Coalition, an astounding 78 percent of freight rail stations are captive to a single major railroad—and captive shippers who use those stations are 100 percent at the whim of the railroads’ market power and dominance. This phenomenon has resulted in skyrocketing prices and diminished service. In 2009, I was sworn in as Chairman of the STB, the independent federal agency charged with regulating the U.S. freight rail network. The STB serves as umpire between freight railroads and the thousands of manufacturers, producers, and farmers who need railroads to ship goods across the United States. As Chairman, I was constantly hearing complaints from rail shippers and their political allies on Capitol Hill, emphasizing that the STB needed to find a way to increase competition among railroad companies. I came to realize that these shippers had a compelling reason to push for change in how the STB did its job. Competition was indeed missing in the marketplace of Class I freight rail, and the STB’s practical ability to regulate unfair rates had become severely limited. As a result, I came to believe that the Board needed to reexamine the state of competition in the rail industry. In 2011, I asked for comments and held a hearing on the state of rail competition, which led an industry group to file a petition to allow for reciprocal shipping. This approach would let captive shippers switch their cargo from one railroad to another along the route—reintroducing competition among railroads. In 2016, the STB issued a proposed rule to make this approach a reality. Unfortunately, that proposed rule has never been formally adopted and remains in limbo to this day. The railroads have claimed that reciprocal shipping could lead to financial ruin, but I have full confidence in the STB’s ability to balance railroads’ need to earn adequate revenues against captive shippers’ need for fair transportation rates. Reciprocal switching has been used in Canada without the collapse of that country’s extensive railroad system—in fact, Canadian railroads are considered among the most efficient in the world. The railroads may be looking at these proposed changes from the wrong perspective. Reciprocal switching could lead to growth in carload traffic if used to provide shippers with more frequent service at their facilities. The reintroduction of competitive transportation costs would allow railroads to compete better with other modes of transportation, specifically trucks and ships. The current truck driver shortages and seaport bottlenecks pose a unique opportunity for U.S. railroads to fill the gaps and inefficiencies in our supply chain. From the perspectives of both railroads and shippers, reciprocal shipping is an opportunity for growth. Railroads can increase revenues while shippers benefit from competition. Although change is always hard, now is time to employ reciprocal switching to benefit the entire U.S. economy. In March, the STB held a virtual hearing on the reciprocal switching proposed rule from 2016. A decision on this matter is expected soon. Reciprocal switching could be a great opportunity for rail shippers if the proposed rule is adopted in some form that makes this remedy available again.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.128
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1280.019

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.

Opus teacher head0.011
GPT teacher head0.220
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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