Bibliographic record
Abstract
The increasing wave of protectionism in various corners of the world with the use of seemingly attractive but economically misleading slogans (shortening supply chains, onshoring, reshoring, nearshoring, friend-shoring, reindustrialization, and ending/correcting ‘hyperglobalization’, etc.) creates a serious challenge to the global trading system and global economic development. Trade and financial transactions have also become victims of the increasing number of geopolitical conflicts and tensions, both ‘hot’ and ‘cold’. Before it becomes too late, i.e., before the current trade tensions go too far and create the hardly reversible spiral of trade and financial wars, retaliations, etc., it is desirable to reflect on what can be lost due to protectionism. This essay analyzes four areas that have benefited from global economic integration since the 1980s (economic growth, poverty eradication, reduction in global economic inequalities, and disinflation) and may suffer from its reversal. It also discusses potential remedies that may help stop a protectionist drift.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.016 | 0.037 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".