Bibliographic record
Abstract
This paper analyzes the job postings in the construction sector to identify the nature and demand of the soft skills that the construction industry is looking for in the employees. The construction industry, like other industries, is witnessing rapid change in the working environment because of factors such as increasing cultural diversity of the workforce, advancement in information technology and introduction of automation and artificial intelligence to perform routine jobs. Because of these changes, individuals need soft skills to succeed in the 21st century workplace. The job postings on the Job Bank Canada website in 2019 that were related to the construction sector were analyzed to identify the soft skills that are in demand in the construction industry. Out of the top 12 skills extracted from the job postings, seven skills are identified as the soft skills. “Team player” is the most sought-after skill in the construction industry, which appears in 56.4% of the job postings. Other soft skills in demand are “effective interpersonal skills,” “excellent oral communication,” “attention to detail,” “reliability,” “flexibility,” and working in a “fast-paced environment.” The analysis shows that the interpersonal and intrapersonal soft skills are in high demand in the construction industry.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".