Research Trends and Hotspots in the Integrated Science Curriculum (1947–2024): A CiteSpace Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integrated science curriculum has become a central theme in global education reforms, yet its research development remains fragmented.This study employs CiteSpace 6.3 to conduct a scientometric analysis of 350 publications retrieved from Web of Science, Scopus Abstract and Citation Database, and China National Knowledge Infrastructure (1947-2024).Publication trends reveal three phases: marginal development (1947-1995), gradual growth (1996-2005), and rapid expansion linked to STEM initiatives and the NGSS (2005-2019), followed by a decline after 2020 due to the COVID-19 pandemic.The United States, China, and Canada dominate contributions, with the Texas A&M University System and the Purdue University System identified as leading institutions.Author and institutional networks highlight active but regionally clustered collaborations.Keyword co-occurrence indicates that curriculum design, student learning, and teaching practices remain consistent research themes.Keyword clustering demonstrates interdisciplinary expansion into sustainability, computer science, and nanoeducation, reflecting broader societal and technological agendas.Keyword burst detection identifies recent surges in "science curriculum" and "students" (2021-2024), signaling growing emphasis on curriculum innovation and learner engagement.These findings provide a systematic visualization of integrated science curriculum research hotspots, offering valuable insights for both future scholarship and educational policy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it