The Relational Space of Teacher Aides and Teachers: The ‘Ins’ and ‘Outs’ of Inclusive Education
Bibliographic record
Abstract
Inclusive education is vital for the rights of all children to education to be met and teacher aides are key players in inclusive education efforts. But inclusive education policy aspirations, like other policies, often fail to be fully realised. In this article we focus on the inclusion of teacher aides in educational networks and the extent to which both teacher aides and teachers can access, borrow, and leverage each other's resources. Our investigation drew on social capital theory and social network analysis to provide insights into this aspect of inclusive education. We administered a social network survey to 701 educators in two communities of learning—comprising four and eight schools respectively. Our analysis involved whole network statistics, analysis of TA-inclusive dyads, centrality measures, statistical tests of the centrality measures, core-periphery analyses, and sociograms. We found that patterns of relational activity between TAs and other educators were low; connections (of any kind) were infrequent. While teacher aides were accessible to others from a network perspective, people did not directly access them. They were, despite policy aspirations to the contrary, rarely considered valued sources of knowledge and expertise or identified as collaborators. In most schools, teacher aides were on the periphery of the network. It is clear that ambitious and well-intentioned inclusive education policies are not yet working as intended. We argue for the vital contribution of the relational space—the relational ties amongst teachers and teacher aides— to realizing inclusive education goals. In turn, we argue for educational leadership focused on belonging and inclusion not only for students, but also for all of the adults who support inclusive education aspirations in school communities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".