AI integration and employment in construction: Exploring positive and destructive effects through a PLS-SEM lenss
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
This research examines how artificial intelligence (AI) integration has affected employment in China’s construction industry. This research builds on the theories of skill-biased technological change and creative destruction to study how AI influences both positive and negative employment effects that later influence overall employment. The report confirms, based on the survey data and by using PLS-SEM, that AI introduction results in both job growth and job losses for managerial-level employees. In addition, whether an organization is ready greatly affects how these relationships play out, improving good outcomes and reducing the bad ones. It is clear from the findings that preparing a strategy helps make the most of AI and alleviate its risks. The study contributes to a more detailed view of AI’s effects on jobs and supplies ideas for sustaining both innovation and employment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.001 |
| 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