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Record W3122342867 · doi:10.1080/09638199.2014.931450

Technology transfers and industry closures

2014· preprint· en· W3122342867 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of International Trade & Economic Development · 2014
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsMcGill University
Fundersnot available
KeywordsProductivityWelfareEconomic welfareWelfare economicsEconomicsEconomyMarket economyEconomic growth

Abstract

fetched live from OpenAlex

There has been a shift of manufacturing industries from Organization for Economic Co-operation and Development (OECD) countries to emerging countries. In a competitive global economy increases in productivity in any country are generally welfare-enhancing. The established industrialized countries can suffer from the collapse of some industries, and from the associated increase in unemployment. We model this process and analyze the interactions between various rigidities that cause it, such as the minimum viable scale of an industry or the number of workers who lack the necessary skills to change jobs. When, under free trade, the technology transfer causes the manufacturing industry to collapse in the home country, it experiences a discrete drop in welfare and the price of the manufactured good rises sharply. Further transfers may reverse these results. The optimal level of protection is the minimum size required to operate. Conditions that make supporting an ailing industry worthwhile can be interpreted in several ways but the conclusion is inescapable: technology transfers fundamentally affect arguments for industry protection at home.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.233
Teacher spread0.205 · 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