Workforce Challenges Posed by the COVID-19 Pandemic: YouTube as a Data Source
Bibliographic record
Abstract
The COVID-19 pandemic is one of the biggest challenges the world has faced in recent decades. The pandemic has disrupted human life in significant ways. Similar to many other industries, the construction industry has faced numerous challenges due to the pandemic. Some of the challenges include project shutdowns, supply chain disruptions, health and safety challenges, project delays, and economic hardships. This study is focused on unveiling workforce-related challenges the industry experienced during the early stages of the COVID-19 pandemic. The investigation leveraged YouTube as the source of information given the presence of rich and relevant content. As a first step, YouTube’s application programming interface (API) was used to extract relevant videos using appropriate keywords. The analysis of the videos revealed a number of workforce-related challenges that the pandemic posed. These included job losses, lower wages, financial stress, and fringe benefit losses. Additionally, the videos highlighted the challenges of migrant workers from different regions, including the US, Canada, Europe, Singapore, the Middle East, and others. The videos also unveiled the struggle of minority workers such as Hispanic workers in the US. The findings of this study can be utilized by industry stakeholders and governments to overcome workforce-related challenges during similar emergencies in the future.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".