What Is Inclusion And How Is It Influenced By Administration: Narratives From 3 Early Childhood Educators
Bibliographic record
Abstract
Through this study, I aimed to explore ECEs perceptions of inclusion and their perspectives on how their understandings and practice are influenced by the administration at their work setting. I used theories from Disability Studies to shape my study and analyze my findings. I sampled 3 participants, 2 of whom were previous ECEs and 1 who was currently an ECE. I held 3 individual interviews where I used an interview guide to ask ECEs about their stories around the research questions. Then, using thematic coding and inductive reasoning, I arrived with 3 themes; definitions of disability, perceptions of inclusion, and understandings and opinions towards administration. I discussed the findings while arguing that ECEs definitions of disability influence their perceptions of inclusion. Which then leads them to seek specific supports from administration. I used these discussions to humbly suggest some implications for ECEs.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".