Bibliographic record
Abstract
The ecommerce industry has become a cornerstone of the modern economy, transforming how businesses operate and consumers shop. In the United States, this sector has experienced significant growth, fueled by factors like increased internet access, the rise of mobile commerce, and the COVID-19 pandemic, which accelerated the shift to online shopping by approximately five years. However, the ecommerce landscape is dynamic, and businesses must constantly adapt to new challenges and opportunities. The new US Administration in 2025 has introduced policies that could significantly impact the ecommerce industry. These policies include new tariffs on imports from Canada, Mexico, and China, as well as changes to the de minimis exemption, which previously allowed low-value shipments to enter the country duty-free. These policy changes have raised concerns among ecommerce businesses and industry experts about potential disruptions to supply chains, increased costs, and reduced consumer demand. This research paper aims to provide a comprehensive analysis of the effects of the new Administration's policies on ecommerce in the US. It will examine the current state of the US ecommerce industry, including key metrics and trends, and discuss the potential implications of the new policies for businesses, consumers, and policymakers.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".