MANAGERIAL STRATEGIES TO ENSURE ACCESS TO EDUCATION IN THE PANDEMIC CONTEXT
Bibliographic record
Abstract
The article presents the conclusions of a research carried out in 2021, in Iaşi County, in order to identify the managerial steps used by disadvantaged school principals in order to ensure access to education for all children. The data collection strategy aimed at a mixed design (quantitative and qualitative) and took place in six disadvantaged schools in Iaşi County, three in urban areas and three in rural areas. Approximately 5,000 children study in these schools, from preschool to high school. 18 databases were analyzed on students’ school results, the remedial programs implemented and the digital technological resources used by schools to ensure equal access to education. The quantitative data were correlated with the qualitative data obtained from the six interviews conducted with school principals. The research findings illustrate that the COVID-19 pandemic has exacerbated problems with access to education. Thus, a quarter of students studying in the six schools included in the research (1167 children out of a total of 4714) studied in the first semester of the 2020-2021 school year only through educational packages and did not benefit from online courses, interaction with colleagues and teachers for an entire semester. Educational projects have been initiated to reduce the negative effects, but the problem remains with lasting consequences.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".