Inclusive Change in Saint Vincent and the Grenadines: A Collaborative Autoethnography
Bibliographic record
Abstract
In many countries, including those in the Caribbean, there has recently been an increasing demand for professional development on inclusive educational practices This need for high-quality professional development is in line with the United Nations Educational, Scientific and Cultural Organization’s (2016) call for inclusion of all children by 2030. Recently, our team was asked to provide professional development to educators in Saint Vincent and the Grenadines that focused on evidence-based practices for students with special education needs. Over the course of 3 years, this service work evolved into a multi-faceted pilot of inclusive education, whereby students with special education needs transitioned from a segregated school into mainstream schools. In this article, we present a collaborative autoethnography that highlights our collective experiences. Our self‑reflections chronicle our experiences and accompanying perceptions gained through providing support and education to educators, students, schools, community, and families in Saint Vincent and the Grenadines over a 3-year period, as they prepared for this transition to inclusion. In collectively reviewing our self-reflections, we discovered three major themes at the heart of our service work: (a) “barriers to inclusion,” (b) “the importance of relationships,” and (c) “transformation.” In discussing these three themes, we explore the successes and challenges we experienced throughout these service projects. What follows is a discussion of our reflective musings related to these experiences as shared critical knowledge for sustainable inclusion work within the Caribbean and beyond.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.020 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".