The Case for Re-shoring Manufacturing Jobs
Bibliographic record
Abstract
Objectives . Retaining manufacturing jobs in Asia based on labor cost alone no longer makes sense. Moving production jobs from low-wage areas to locations closer to markets and engineering design centers, or re-shoring, is a significant trend, at least in the United States. Articulating a literature-based rationale for locating jobs near markets and supporting this rationale by interviewing experts could assist businesses to reconsider their past policies and strengthen national economies. Methodology . Investigators evaluated academic literature and the trade press mostly reporting on North American Free Trade Act (NAFTA) Partners (U.S.A., Canada, Mexico) on the issue of re-shoring manufacturing jobs. In addition, researchers interviewed executives and engineers in companies currently importing auto parts from Asia, but considering domestic sourcing. Findings . About one-third of all manufacturing companies in the US and fully half of large companies were actively considering re-shoring. Product quality is more easily assured with geographic proximity between the supplier and the original equipment manufacturer that will assemble and ship the final product. Long supply lines are expensive and counter-intuitive in a just-in-time manufacturing climate. Criteria for re-shoring are dynamic. Research Limitis . This is small attempt to address an issue of enormous proportions. The interviews were limited to the auto industry. More, larger-scale interdisciplinary research is needed. Pratical Implications . Understanding this phenomenon is critical. The economic stakes are high for those countries largely consuming the manufactured products which are produced and generating employment elsewhere. Originality of the study . Little literature exists on this recent phenomenon. Many manufacturing companies are reconsidering their sourcing decisions and research such as this could contribute to re-shoring jobs and helping local economies.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 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".