A study of AMT in North America
Bibliographic record
Abstract
Purpose The purpose of this paper is to report the findings of an exploratory survey administered in North America on advanced manufacturing technologies (AMTs). The objective of the survey is to compare the status of AMT investment, planning and implementation, and performance in two different regions: Anglo America (developed countries) and Middle America (developing countries). Design/methodology/approach Responses from 97 Anglo‐American companies (62 from Canada and 35 from the USA) were compared to responses from 125 Middle American companies (85 from Mexico and 40 from Costa Rica). The researchers used different statistical analyses such as exploratory factor analyses, analysis of variance and regression. Findings In general, Middle American countries representing the developing region show higher AMT investment, planning and implementation activities, and finally, higher performance due to AMT implementation. This phenomenon was hypothesized since developed countries have shifted most of their manufacturing operations into developing countries, while they keep ownership of multinational firms. Therefore, big corporations that previously invested in AMT in their home countries are now investing in AMT in their manufacturing plants in developing countries. Originality/value This research provides insights to the growing body of knowledge on AMT. Most AMT research has been done in developed countries. In this study, the researchers show results comparing developed versus developing countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".