Traders of the Lost Ark: Rediscovering a Moral and Economic Case for Free Trade
Bibliographic record
Abstract
The basic principles of a free society are timeless, but they need to be relearned every generation. One of those principles is the freedom for people to trade freely with one another — both within nations and across borders. The case in favor of free trade has been uncontroversial among economists since the time of Adam Smith, but support among policy makers and the public has ebbed and flowed with the political winds. Now, with the Trump administration raising tariffs and other trade barriers against Canada, Mexico, the European Union, and China, the ongoing liberalization process that began in the aftermath of World War II is experiencing the greatest threat it has yet faced. As of this writing, it is too early to tell if the administration’s actions are a blip in a longer trend or mark the start of a full-on reversal. Either way, the time has come to restate the economic and moral case for free trade for the current generation of policy makers and the interested public. With that as its aim, this paper provides a comprehensive case for the free exchange of goods and services without government restraint. It makes the case that people should be free to trade with whomever they want, whenever they want, and however they want, without government interference. In addition, it offers a general explanation of how global trading works, places that discussion in historical context, provides a taxonomy of the different types of trade barriers, and outlines the arguments for and against particular trade policies. Most importantly, it makes the case for free trade as a policy that is inherently moral, in addition to being economically sound.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.012 | 0.021 |
| 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".