Action research through art: Social studies in early childhood
Bibliographic record
Abstract
Early childhood social studies education prepares children for global citizenship by providing them with democratic values and social skills. Social studies is a field in which children learn from experience in their daily lives and sometimes gain awareness through planned learning processes. Through action research methods, this study aims to equip early childhood children with knowledge and awareness in areas such as poverty, family differences, and special needs. During the course of the research process, researchers collected data using a variety of approaches, including observations of children, interviews with teachers, researcher diaries, comments from other members of the school staff, and the opinions of the validity committee. The collected data were analyzed in two stages. In the first stage, the inductive analysis method was used to determine the focus group and identify the problems. The previous themes guided the use of deductive analysis in the action plan process. In this context, the study’s main participants were 15 children in need of intervention and their teachers. As a result of the intervention applied to children through collaborative action research, great changes emerged in children’s perceptions of the differences in their environment.
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 | 0.062 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.012 | 0.064 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| 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".