Analysis of Observed Skill Affinity Patterns and Motivation for Multiskilling among Craft Workers in the U.S. Industrial Construction Sector
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
Previous research has shown that multiskilling strategies can increase productivity, quality, and continuity of work and can also help mitigate craft shortages through better utilization of the existing workforce. Using extensive craft certification and skills data, the writers apply correlation and cluster analyses to identify actual patterns of multiskilling among craft workers using two separate data sources. The results of the cluster analysis indicate that current craft skills aggregate into four groups: civil, mechanical, electrical, and general support. It is also observed that acquiring mutually supporting skill set pairs significantly drives multiskilling strategies in practice, thus diminishing the relative impact that duration on project has on driving multiskilling practice, despite its importance in previous literature. Still, comparing the observed multiskilling patterns obtained from the skill affinity analyses with multiskilling strategies proposed by previous studies generally reinforces the potential efficacy of those strategies.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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