The U.S. Harbor Maintenance Tax: A Bad Idea Whose Time Has Passed?
Bibliographic record
Abstract
This article presents a critique of the Harbor Maintenance Tax (HMT) as a flawed method of collecting revenues and dispersing benefits. Looking at how it is applied on the Great Lakes of the U.S., a small container of high-value goods is taxed more heavily than an entire shipload of raw material for steel fabrication. It also details legal challenges in recent years, including a decision that exempted exports from the tax, though imports and domestic items are still subject to it. The result has made the HMT inherently unfair, the author argues, since exports put as much demand on harbor facilities as imports and domestic goods. Also, because it is based on a cargo's value, high-value items are steered away from water-borne transport, which is the most fuel efficient under some conditions. Additional objections and criticism are listed, including difficulty in enforcement, suppression of innovation in water-borne transportation, especially on the Great Lakes, forcing container cargo to Canadian ports and problems with how HMT revenues are distributed. Attempts to address many of these flaws are also detailed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".