Bibliographic record
Abstract
The aim of this study is to analyze the publications in the literature on differentiated instruction. 667 publications published in various sources in the Web of Science database until November 2020 were analyzed using the bibliometric analysis method. Analyzes were made with Vos viewer software. Common keywords used by the authors, the bibliographic coupling of countries, institutions, sources, authors and publications and co-citation of authors, references and sources were analyzed and visualized via the software. As a result of the analysis, it was determined that the concept of differentiated instruction is frequently used together with the keywords as differentiation, curriculum, inclusive education, universal design for learning (UDL), learning styles, assessment, professional development. In the literature of differentiated instruction, Ghent University and Brussel University stand out from the other universities. In addition, “teaching and teacher education”, “teachers and teaching” and “educational leadership” journals that include publications related to the differentiated instruction are prominent journals. As a result of the most influential author analysis, Tomlinson came to the fore as the author of the most cited publications. At the same time, the USA and Canada stand out as the leading countries in publications in the field of differentiated instruction.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.176 | 0.194 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".