Procedure to Reduce Evaluation Time in the Selection of Professional Staff in Medium-Sized Multi-Family Construction Companies Using the AHP Method
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.
Bibliographic record
Abstract
In the construction sector, the selection of personnel for the technical office faces challenges such as the lack of structure and subjectivity in the evaluation criteria, which makes it difficult to quickly identify the most suitable candidates.This article proposes an optimized procedure to address this problem through the use of the AHP multicriteria method and Expert Choice software.The process involves applying AHP to establish priorities and evaluate candidates based on previously defined objective criteria.Through interviews and surveys, deficiencies in the traditional approach were identified, such as the lack of planning and the reliance on subjective judgments.The proposal demonstrates how the application of AHP reduces the evaluation time by 70%, improving transparency, traceability, and reliability of the process, contributing to the formation of technical teams more aligned with the specific requirements of the projects.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it