The Landscape of Research on Contextualized Science Learning: A Bibliometric Network Review
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
ABSTRACT The vast and rapidly growing amount of science education research makes it challenging for researchers to navigate and synthesize developments across the field, particularly concerning broad concepts evolving along divergent paths. To address this issue, a novel review methodology employing bibliometrics and network analysis was tested to identify and characterize clusters of research focused on the relationship between school‐based science learning and contexts where that science is applied, experienced, observable, or otherwise relevant (e.g., socio‐scientific inquiry, place‐based learning, culturally‐responsive pedagogy). Using a sample of 935 academic papers, the bibliometric network analysis revealed the landscape of contextualized science learning research, identifying 13 distinct clusters of scholarship. Bibliometric and qualitative data were used to describe the research trends within clusters and confirm they were conceptually meaningful and distinct. This methodology facilitated greater understanding of how research can become clustered into “invisible colleges” over time, offering a synthesis approach to grasp interrelated lines of research within an evolving landscape. The methodology has potential to identify other schools of thought or overarching themes in science education, enhancing researchers’ ability to perceive the field as a coherent landscape of interconnected ideas or to identify specific research trajectories within a broad concept.
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 | 0.049 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.244 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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