Understanding gaps in research networks: using “spatial reasoning” as a window into the importance of networked educational research
Bibliographic record
Abstract
This paper finds its origins in a multidisciplinary research group’s efforts to assemble a review of research in order to better appreciate how “spatial reasoning” is understood and investigated across academic disciplines. We first collaborated to create a historical map of the development of spatial reasoning across key disciplines over the last century. The map informed the structure of our citation search and oriented an examination of connection across disciplines. Next, we undertook a network analysis that was based on highly cited articles in a broad range of domains. Several connection gaps —that is, apparent blockages, one-way flows, and other limitations on communications among disciplines—were identified in our network analysis, and it was apparent that these connection gaps may be frustrating efforts to understand the conceptual complexity and the educational significance of spatial reasoning. While these gaps occur between the academic disciplines that we evaluated, we selected a few examples for closer analysis. To illustrate how this lack of flow can limit development of the field of mathematics education, we selected cases where it is evident that researchers in mathematics education are not incorporating the important work of mathematicians, psychologists, and neuroscientists—and vice versa. Ultimately, we argue, a more pronounced emphasis on transdisciplinary (versus multidisciplinary or interdisciplinary) research might be timely, and perhaps even necessary, in the evolution of 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 | 0.039 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.019 | 0.071 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| 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".