Bibliographic record
Abstract
Covid-19 is going to have a profound impact on how we manage our work. The increasing tendency to decouple the workforce from the workplace is creating both challenges and opportunities. Amidst fear of decline in staff productivity, experience of this past year shows that employees working from flexible locations, including their own home, are becoming more productive than previously thought. Four major shifts are taking place in the world of work. Firstly, the concept of fixed location of an office is giving way to the idea of flexible locations leading to a reconfiguration of the traditional office. Secondly, managerial focus has moved from efficiency to resilience. Thirdly, control as a management principle is losing ground to trust leading to disintermediation and de-layering of decision-making. Finally, organizational leaders are increasingly emphasizing the need to complement technical skills with social Skills. Much innovation is taking place in all these areas. These shifts are happening not just because of Covid-19. They were already set in motion; the pandemic has accelerated them. Work from home is a good response to the pandemic, but it cannot be an alternative to the office for ever. Going back to the nineteenth century idea of office as a fixed location is neither efficient nor desirable. What we need is a hybrid model. Based on a review of national and international practices adopted as a response to Covid-19, this article argues that the pandemic has given public-sector agencies an opportunity to use available technologies for improving business processes through flexible working arrangements, including the hybrid model. And this process has already started. In many countries, public-sector organizations are catching up with the private sector in terms of introducing the hybrid model. We in Nepal can learn much from this and adapt some of these practices to our specific socio-economic and cultural context.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".