Information Competencies of Information Technology Specialists in the Conditions of the Pandemic: The Algorithms of Formation and Features of Development
Bibliographic record
Abstract
The research has examined scientific developments and systematized practical data in the field of formation and development of information competencies of digital technology specialists in the conditions of the pandemic. This has become the research purpose of the academic paper. Digital transformations in the sphere of economy, management, social relations and production have caused significant changes in the methods and forms of training of specialists in the IT industry, and the research is focused on changes in the methods of organizing the educational process. The specifics of the evolution of teaching philosophy and methodology in the conditions of the pandemic are not gradual transformations, but the urgent need for rapid changes caused by a crisis situation in the conditions of quarantine restrictions. Unexpected changes require quick but qualitative changes. Distance learning technologies turned out to be relevant during the pandemic not only in the educational space of Ukraine; such changes took place in educational systems around the world. In order to study the issues outlined, a complex of bibliosemantic and analytical methods was used in the research.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".