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Record W4379624534 · doi:10.1353/iur.2014.a838565

Focus: The workers scorecard on NAFTA

2014· article· en· W4379624534 on OpenAlexaboutno aff
David Bacon

Bibliographic record

VenueInternational Union Rights · 2014
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFactory (object-oriented programming)PopulationBusinessPolitical scienceEconomic growthSociologyEconomics

Abstract

fetched live from OpenAlex

INTERNATIONAL union rights Page 7 Volume 21 Issue 3 2014 The promises of profits from increased investment and freer markets were kept. But the promises of jobs and benefits for working people were not electronics, apparel and other goods moved to Mexico, and job losses piled up in the United States, especially in the Midwest where those products used to be made”. In a 2006 report Scott said those deficits “displaced production that supported 1,015,291 US jobs since NAFTA took effect”. In the last few decades Detroit lost half its population as the auto industry left, and today every engine in a Ford comes from Mexico. Huge swathes of other industrial cities have acquired that abandoned look that comes with boarded-up homes and storefronts. But the working families who lost those outsourced jobs didn’t disappear. Instead, hundreds of thousands of people began an internal migration within the US larger than the dustbowl displacement of the 1930s. Former machinists and factory workers went on the road, got jobs in fast food restaurants, or lost their families and began living on the streets. Fear of losing a job is never far from any worker ’s mind, but in some industries fear has become terror. Employers bent on lowering wages or cancelling health care plans quickly learned to use NAFTA to inspire that fear. In 1997 (three years after NAFTA went into effect) Cornell professor Kate Bronfenbrenner found that one out of every ten employers facing a union drive said they’d move to Mexico. ‘At ITT Automotive in Michigan’, she reported, ‘the company parked 13 flat-bed tractor-trailers loaded with shrink-wrapped production equipment in front of the plant for the duration of the campaign with large hot-pink signs posted on the side which read ‘Mexico Transfer Job’’. By 2009 a second report, No Holds Barred, found that 57 percent of employers facing a union election threatened to close their worksite. During the NAFTA period, US wages have remained virtually flat. While factors beyond NAFTA (such as the falling rate of unionisation) had an impact, NAFTA and subsequent trade agreements clearly contributed to it. “Production workers’ wages have suffered in the United States”, says Scott. In his 2006 study he found “there is a nationwide loss of $7.6 billion in wage premiums that would have been earned had trade been balanced”. Jeff Faux, former director of the Economic Policy Institute adds simply, “NAFTA strengthened the ability of US employers to force workers to accept lower wages and benefits”. AFL-CIO President Richard Trumka called NAFTA “only the first in a series of trade agreements that have undermined millions of middleclass American jobs and weakened our democratic structures. So it is ironic”, he said, “that this year the supporters of that failed model are bringing forward a fast track trade promotion bill to bring us more of the same: more trade deals I n 1986, a provision of the Immigration Reform and Control Act created a commission to investigate the causes of Mexican migration to the US. When it made its report to Congress in 1992 it found, unsurprisingly, that the biggest was poverty. It recommended the negotiation of a free trade agreement, modelled on the one that had been implemented a few years before between the US and Canada. The commission argued that opening the border to the flow of goods and capital (but not people) would, in the long run, produce jobs and rising income in Mexico, even if, in the short run, it led to some job loss and displacement. The negotiation of the North American Free Trade Agreement began within months. When completed, it was sold to the public by its promoters on both sides of the border as a migration -preventing device. During the debate executives of companies belonging to USA•NAFTA, the agreement’s corporate lobbyist, walked the halls of Congress, wearing red, white and blue neckties. They made extravagant claims that US exports to Mexico would account for 100,000 jobs in its first year alone. Some sceptics warned that the agreement would put downward pressure on wages and encourage attacks on unions, because its purpose was to create...

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.036
GPT teacher head0.378
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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