ASSESSING EDUCATIONAL ENVIRONMENTS USING SACERS INTERNATIONAL SCALES: A BIBLIOMETRICPERSPECTIVE
Bibliographic record
Abstract
To establish optimal conditions for school-age learners’ best educational outcomes, research-evidenced documentation is a requirement prior to any significant change. This bibliometric analysis investigated articles published on School-Age Care Environment Rating Scale (SACERS) from 2017 to 2023 (n=10). Data collection involved identification, screening, exclusion, and eligibility stages. The bibliometrics R-package was used for data analysis on the Bibliometric cloud-based platform, focusing on publication patterns, citation networks, and bibliographic insights. Key findings indicate limited research-related publications on SACERS, possibly due to country-specific adaptations and variants in local languages. The scientific production varied annually, with few publications during 2017-2023. Canada, Russia, and the USA led SACERS research, implementing changes based on findings in target educational institutions. It was also found that research publications imply a university’s intellectual and epistemological contribution; this also offers insights for academic institutions to enhance research strategies and academic influence. Based on these findings, we concluded that SACERS is an invaluable tool for globally evaluating educational environments. Its comprehensive assessment empowers educators to foster enriching learning environments for all students. Keywords: SACERS scale, assessment, school-age children, bibliometrics, educational conditions.
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 | 0.033 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.076 | 0.087 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".