A Safety net to Foster Support for Trade and Globalisation\nInternational Survey on Attitudes towards Trade\nand Globalisation 2018. Bertelsmann Stiftung GED Study \n
Bibliographic record
Abstract
Given the rise of protectionist reflexes and a world on the brink of trade war, a survey by the Bertelsmann Stiftung of attitudes towards trade and globalisation gauges the temperature among people in twelve developed and emerging economies. It finds that attitudes are generally positive - much more so than various sources of discon-tent raised in the survey.\nIn the emerging countries 64 percent believe that globalisation is a force for good. Support in the developed econ-omies is still large with a relative majority of 44 percent seeing globalisation as positive (25 viewing it as a force for bad). Support for increased international trade is even larger: In emerging economies 73 percent believe that trade is positive for their own country, almost matched by 69 percent in developed countries. The most enthusias-tic pro-trade countries were Russia, India and Indonesia among the emerging economies and Canada and the UK among the developed ones. Turkey and France are the most sceptical about international trade. Respondents believe that globalisation and trade particularly benefit growth, companies, consumers, product prices and job creation.\nHowever, the survey also uncovers several sources of discontent that ought to be taken seriously. Generally, people are sceptical about the effects of globalisation and trade on job security, wage increases and product quality. While generally sympathetic to foreign direct investment, they do not believe takeovers of domestic com-panies by foreign investors to be beneficial. A key finding of this survey is that many do not feel sufficiently well protected by their governments against any negative side-effects of globalisation: in developed economies, 49 percent do not feel adequately protected (against 27 percent) while in emerging economies opinion is tilted slightly towards the opposite direction: 50 percent feel sufficiently protected while 40 percent hold the opposite view.\nThe survey also asked participants to list the most/least preferred trading partners. Japan and Germany lead the list of countries with which people believe trade to be most beneficial. China leads the list of least preferred trad-ing partners. Similarly, respondents were asked to rank who they believed to be the country/region most benefitting/suffering from globalisation. The USA emerge as the perceived winner of globalisation, closely fol-lowed by China. The list of losers is headed by Africa, followed – tellingly - by the USA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".