Differences in Perspectives regarding Labor Productivity between Spanish- and English-Speaking Craft Workers
Bibliographic record
Abstract
The influx of Hispanic workers helped the U.S. construction industry alleviate its shortage of craft workers in the last decade. In 2009, Hispanics accounted for nearly a quarter (22.5%) of the construction workforce in the United States. However, no research has been conducted to examine how various factors influence Hispanic craft workers’ productivity. This paper analyzes the data from a nationwide survey to obtain craft workers’ perspective on construction productivity. The respondents were categorized as Spanish- or English-speaking workers according to their declared primary language, irrespective of their ethnic background. The findings reveal that Spanish- and English-speaking craft workers generally agreed on the priority of the factors affecting labor productivity. However, Spanish-speaking workers rated factors associated with supervisor direction, safety, and labor more severely than English-speaking craft workers. Meanwhile, English-speaking craft workers perceived factors related to engineering drawing management as being more detrimental to productivity than did Spanish-speaking craft workers. Specifically, in comparison with English-speaking craft workers, Spanish-speaking craft workers experienced more severe issues with communicating with their supervisors, pay and monetary bonus for good performance, and lack of training on safety, health, and skills. These findings should be valuable for project management to effectively improve labor productivity of their Spanish-speaking craft workforce.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".