Datos y evidencias del teletrabajo, antes y durante la pandemia por COVID-19
Bibliographic record
Abstract
This short essay starts from the hypothesis that teleworking is nothing more, and nothing less, than the manifestation of an announced change of time, of which the pandemic is acting as an accelerator. A change of era defined by a new economic and labor space that is cyberspace, which deepens the digitization of the economy and the flexibilization of the labor market. Teleworking is an expected result in this new reality. The pandemic has exponentially increased this new form of work organization, defined as work done at home using electronic equipment. From a global perspective, the ILO has estimated, based on household surveys of 31 countries carried out in the second quarter of 2020, that 17.4% of the employed people worldwide, some 557 million, worked in that sector. period in their homes, ranging from 25.4% in high-income countries to 13.6% in low-income countries. For Latin America, teleworking rose between 25-30% in the second quarter of 2020, and in Europe, Eurofound, in April 2020, estimated that 37% of participants had started working at home with the onset of the pandemic. All of which has made it possible to maintain certain economic activity and the employment relationship of these people during the pandemic. Likewise, it should not be forgotten that the pandemic has also caused huge job losses, especially during the second quarter of 2020, when, according to ILO estimates, more than 300 million full-time jobs were lost. Job losses that as of the 2nd quarter of 2021 have not yet recovered from pre-pandemic levels. In this sense, it should not be forgotten that teleworking does not create new occupations, it only provides a new way of organizing work for those occupations whose tasks can be performed virtually. At the time of writing this article, after a year of restrictions on economic activity, mobility and social interaction, the surveys that Eurofound has continued to carry out show that exclusive teleworking, every day of the week, is decreasing in the whole of the European Union, from 34% in summer 2020 (second round) to 24% in spring 2021 (third round). Given that the pandemic has not yet ended, and we do not know how the "experiment" will end, we must continue to monitor these changes in the way of working, and how they affect the labor market and employment and working conditions. As far as we know, teleworking offers great advantages, but also important disadvantages, with respect to working and employment conditions, which can, positively or negatively, affect the health of the teleworker. Telework regulation is a key element of cyberspace-based regulation of the digital economy, and it must be a global issue.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".