Who cites whom? U.S.-American authored research syntheses in the field of educational technology: a bibliometric analysis
Bibliographic record
Abstract
Research syntheses are an important approach to capture and synthesize empirical studies in educational technology. However, despite their proclaimed impartial summary of available research, imbalances exist as to whose research is included due to publication language or in regard to the visibility of entire scientific communities.Using the concepts of academic hegemony and WEIRD research, a bibliometric analysis is conducted in order to explore how research syntheses of authors located in one of the so-called academic core countries – the U.S.A. – are positioned in international comparison, and how this potentially shapes the discourse on educational technology.For the bibliometric analysis, a corpus with N = 446 research syntheses is considered, comprised of 95 U.S.-authored and 351 non-U.S.-authored syntheses. Findings reveal that U.S.-authored syntheses are relatively self-referential and also draw heavily on databases of U.S.-based professional societies in their literature search. Over half of the syntheses cite other U.S.-based research, followed by Chilean, British, Canadian, Australian and German research. In contrast, U.S.-authored syntheses are cited globally, accentuating their perceived importance and influence. Findings point to the need to consider underlying influences and contextual factors for research syntheses in educational technology, reflect on citation practices and generalizability of findings from educational research.
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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 | 0.030 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.098 | 0.138 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| 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".