A FRESH LOOK AT KNOWLEDGE MANAGEMENT STRATEGIES DURING THE CRISIS
Bibliographic record
Abstract
The pandemic has enhanced the social function of digital technologies and services. It is solely through digital technology that a massive shift to remote work has been possible during the most difficult period of the pandemic. All over the world, the philosophy of office work is changing, and there is a transition to permanent and conditional-permanent remote work. For example, Transport Canada is planning to move to telecommuting as a key employment model for its employees. In the near future, telecommuting will continue for most of the 6,000 employees in the agency. In China, widespread use of WeChat, Tencent, and Ding digital working applications began in late January 2020, when isolation measures were introduced. In Switzerland, COOVID-19 Remote Work and Study Resources provides free resources for remote operation and distance learning. Zoom and Google Meet videoconferencing, remote workplaces, and new social platforms run remote work almost immediately, and this trend is likely to continue after the lifting of the quarantine. Trends in staff employment worldwide are rather mixed. According to LinkedIn, it is possible to track changes in the employment rates of seven key economies – Australia, China, France, Italy, Singapore, Great Britain and United States. In France and Italy, the decline was more pronounced at -70% and -64.5% respectively by mid-April 2020. Since then, employment has been gradually recovering, and most of the seven key economies for which these figures have been analysed tend to change by 0 per cent year on year. By July 1, 2020, China, France, and the United States had seen the largest rebound in relative recruitment – -6% or -7%. At the end of September 2020, the countries with a high recovery in employment were China (22 per cent), Brazil (13 per cent), Singapore (8 per cent) and France (5 per cent). In these economies, hiring so far seems to compensate for months in which no new personnel have been recruited, indicating some stabilization of the labor market.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".