“Six Faces of Globalization: Who Wins, Who Loses, and Why It Matters”, Anthea Roberts and Nicholas Lamp, Harvard University Press, 2021: overview
Bibliographic record
Abstract
This book [Roberts and Lamp (2021), henceforth R&L] "offers a meta-framework for understanding Western debates about economic globalisation" (p.19 K&L).The authors are Australian and Canadian law academics, allowing them to experience globalisation from different lands at the same time.In Britain, Brexit 2016 was the high point of debate about the benefits of globalisation.Later the election of Donald Trump as US President confirmed the concern felt by native citizens about the scale and speed of globalisation.The authors conceive of the globalisation process as a Rubik's Cube with different, but not mutually exclusive, faces.These faces come in the form of competing, and sometimes, complimentary narratives.Narratives are important because we fit facts into narratives.If a particular fact is refuted, no matter, we just look for another one that confirms our chosen narrative.So for R&L, "Narratives are stories we tell ourselves to make sense of the world, but they are also stories we tell others to influence their view of the world" (R&L, p. 28).These are the following:• Establishment narrative in which there are gains-from-trade and we are all better off; even if Chinese people are better off than Germans, or Indians.It is true some good people lose if I devoted my life to oil discovery in an era of rejection of fossil fuels.But for every loser there is an even greater winner, who can help the losing group recoup their losses somewhat.This philosophy might be seen to be embedded in the "end of history" perspective of Frances Fukayama (Fukayama, 2006).For Fukayama, you may be an Indian Hindu and I an Irish Catholic.But, surely, we both want the same things really?Health, happiness and security.And the most efficient way to serve these needs is free-market capitalism, overseen by a democratic State.Indeed, this seemed the message of the fall of the Berlin Wall in 1989.After various escapades in "nation building", in Iraq, Afghanistan and Libya, almost no one believes this.The institutional structures that have served, Britain, the USA, India and Israel so well were a horrific disaster in these lands.This suggests there is more to national building than Free Market + Democracy = Happiness:• Right-wing populist: Doubts about the value of globalism first emerged recently in the UK Brexit debate.Freedom of movement for people worked for Polish plumbers or Italian academics, but did it really work for English kids who left school at 16 years of age?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".