Study on Skills Gap Beyond COVID
Bibliographic record
Abstract
Keeping up with the pace of technological advancement is a challenge for companies of all shapes and sizes. It is increasingly crucial to reskill and upskill in the changing era of innovation, especially post-pandemic (Beyond Covid), and acquiring soft skills is imperative for success in the digital era. The importance of soft skills, like teamwork, communication skills, problem-solving, and critical thinking, is a growing demand, heightened especially during the pandemic while working remotely. Upskilling ensures employees’ skillsets won’t become obsolete. As you reskill your employees, you create a more well-rounded, cross-trained workforce, and increase your team’s effectiveness. (itagroup.com, n.d.) According to the United Nations Department of Economic and Social Affairs, the equivalent of 255 million full-time jobs have been lost due to the pandemic, and 1.6 billion informal economy workers lacking a social safety net have been significantly affected. The recovery will be slow; global economic growth is expected to return to pre-pandemic levels only by 2022 or 2023. The pandemic has dramatically accelerated the need for new skills in the workforce, with social and emotional skills high in demand. The proportion of companies addressing empathy and interpersonal skills doubled in 2020, according to our newest McKinsey Global Survey on reskilling. (McKinsey, 2021)
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".