Being, Belonging, Becoming – a quality of life frame for building resilient inclusive schools for peri- and postpandemic period
Bibliographic record
Abstract
This article describes the concept of quality of life in the equation of quality of inclusive education starting from the Being-Belonging - Becoming approach, a model of quality of life validated by the University of Toronto. The aim of the study is to identify solutions for inclusive schools as a social actor (through educational and social practices) that has to be involved in improving the quality of life of beneficiaries for the peri- and post pandemic period. A semi structured interview (Voice of Beneficiaries) is the main method for a radiography of the quality of education addressed to children with special needs from urban and rural geographical areas during the pandemic period, by questioning 34 experts belonging to several interest groups (16 teachers with expertise, 5 school principals, 8 parents with expertise in the field and 5 representatives of NGOs) about their perception regarding socio-emotional development and well-being of stakeholders and how learning environments/learning and assessment strategies/teacher-student interactions have changed; their improvement proposals about. This process is not designed to be statistically significant, but rather to get ideas that can be important for further analysis. The identified solutions allow us to elaborate of a matrix of educational services listed in the Being-Belonging - Becoming pattern.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".