Bibliographic record
Abstract
Introduction Is there a connection between extremism and the introduction or spread of markets? Has the spread of markets around the world – what is often referred to as “globalization”– fostered or retarded extremism? One way to begin making the connection is via the concept of “transparency.” The fall in the costs of acquiring and transmitting information and of transacting across borders generally is often said to require a global world order in which countries specialize according to comparative advantage and the international division of labor is as complete as possible. In order to facilitate this outcome, economic relations should become as transparent as possible, because greater transparency implies lower transactions costs. A larger global division of labor means an expansion of world trade, and greater transparency facilitates this expansion. Democracy, too, thrives on transparency, and dictatorship on obfuscation. Consequently, on this point of view, it is obvious that the new global world order must be governed by the most transparent systems possible, both to promote democracy and economic efficiency. To some extent, transparency and globalization go together in that both are the result of the information revolution. Of course, transparency is not exactly the same thing as globalization. Indeed, sometimes people associated with the anti globalization movement have been demanding “greater transparency” from organizations such as the IMF, the World Bank, and the World Trade Organization (WTO).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| 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".