Alacrity of Preschool Education Teachers to Work with Children in Inclusive Groups
Bibliographic record
Abstract
To make preschool inclusion successful and to have children gain the expected benefits, teachers need to be provided with functional teacher training programs that foster positive attitudes and provide them with a meaningful experience. The purpose of the study was to investigate the readiness of educators of preschool education institutions in Kyiv to work in inclusive classrooms. In the process of integration into the European community, Ukraine has focused on educational reforms, in particular, the creation of conditions for the introduction of inclusive education in educational institutions. The parameters of the research are taken into account, such as attitude to the idea of inclusion, possession of the necessary arsenal of pedagogical practices, understanding of barriers and ways to overcome them on the way to the organization of inclusive education. A structured questionnaire of preschool teachers attempts to reveal their attitudes towards inclusive education in general and to assess the conditions that promote or hinder the emergence of willingness and willingness to work in an inclusive group. The results of the study showed that a significant number of educators have a negative attitude to the idea of inclusion, believing that children should study in special groups or in special institutions (but not boarding schools). Educators name a number of barriers that prevent them from being positive about inclusive education, and underestimate their readiness for such work.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".