Bibliographic record
Abstract
Abstract The article deals with the phenomenon of digital divide in the education in Canada. The domestic and foreign scientific and educational publications have been studied and analyzed. It has been found out that traditional means for training pedagogical specialists are gradually losing their relevance due to lack of educational dialogue between a teacher and a student. Information and communication technologies have entered today’s youth everyday life and become an essential means of communication, receiving and transmitting information. Based on the source study, the essence and reasons of digital divide have been revealed. Canadian researchers consider that it is possible to overcome this problem by revising the approach to teacher training which will focus on the forming of future teachers’ information and communication competence. Various definitions of the terms “information competence”, “ICT competence”, “digital literacy”, “e-literacy” have been described. The model of ICT competence, its structure and the process of its integration into education have been analyzed. The examples of forming future teachers’ ICT competence in universities of Canada have been given. It has been revealed that the problem of effective ICT implementation into educational activities is in the range of many Canadian studies, but in fact the phenomenon of digital divide in education is still topical due to insufficient activity of teachers of pedagogical faculties and students’ ignoring the problem. A number of studies have been examined, the authors of which give practical recommendations aimed at enhancing the role of new technologies in teacher training in Canada.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".