Poverty Simulation With Teacher Candidates to Increase Awareness About Poverty
Bibliographic record
Abstract
Background: Research suggests that many educators hold inaccurate or incomplete perceptions of poverty leading to stereotyping of students living in poverty. It is important for future teachers to understand more about the complexities of poverty so they can bridge gaps between misconceptions and understanding for their students, families, and school communities. Purpose: This study examined teacher candidates’ preconceived thoughts and changes in perceptions toward poverty based on participation in an experiential learning activity (i.e., a poverty simulation). Methodology/Approach: The participants ( n = 94) were in their final term of a 2-year teacher education program in Ontario, Canada. This mixed-method study used a Community Action Poverty Simulation combined with the quantitative presurvey Undergraduate Perception of Poverty Tracking Survey and qualitative postgroup discussions. Findings/Conclusions: Results indicated that the poverty simulation was an effective tool for disrupting poverty perceptions and myths among teacher candidates and provided insight into further areas to increase understanding. Implications: Based on the findings, poverty simulations are a promising experiential learning process for teacher education programs as a cost-effective, consciousness-raising exercise that can prompt deeper levels of learning for teacher candidates and better prepare them to teach students living in poverty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".