Digitalization of vocational education under crisis conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid development of technologies and their application in all branches of the economy calls for digitalization of education as a prerequisite of improving the quality of vocational training. Digital technologies in their turn allow to diversify the mode of training according to the needs arising under various circumstances. In some countries like Australia and Canada, online and blended learning are the only possibly form of training due to learners’ remotedness to schools. But as recent experience shows, introduction of online education was the only way out to sustain it under the conditions of the COVID-19 and now by the wartime and absence of access to educational facilities. In this was, the necessity of digitalization of education is constantly growing together with its increasing range of applicability. Now all production processes and processes of the service sector are under the influence of digital technologies, because modern machines are operated by computers. Modern military equipment is also digitally based and operated. Thus, working in modern industries and services requires a high level of digital literacy, which presents a challenge for the system of vocational education. Under modern conditions, irrespective of their positive or negative origin, vocational schools (VS) should be ready to train specialists for various spheres of industry capable of working with constantly changing digital technologies. This fact puts forwards certain requirements to digital literacy of both students and teachers, who have to cooperate through digital devices and software to attain the set educational goals. All these circumstances require the equal level of digital literacy of both teachers and students to provide educational institutions with the latest material base and digital resources.
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.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it