Resultados obtidos com a aplicação da polivalência da mão de obra na construção civil: revisão sistemática
Bibliographic record
Abstract
Despite its relevance, the construction industry has been facing the same problems for decades. The lack of skilled labour, workers with few opportunities for professional growth, recurrent delays and high turnover rates are sector’s well known and hard to solve problems. Although important in the context of lean thinking, the concept of multi-skill faces resistance due to the learning effect and uncertanties about productivity. Considering the aforementioned situation, this review aims to compile the main results of studies carried out on the effects of multi-skill, when applied to the civil construction reality, as well as to analyse parameters related to the papers in question. A total of twenty-three publications were found in a systematic bibliometric review. Results are partial and are part of a bigger research effort. It was observed that a multifunctional workforce benefits both employees and employers. A shortage of studies with real work environment results was discovered, with most of the papers focusing on computer models.
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.
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 | 0.029 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".