FORESIGHT FOR EVERY KID \nThe Potential Impacts of Futures Education for \nSocioeconomically Disadvantaged Children: A Cross-Disciplinary Inquiry
Bibliographic record
Abstract
While poverty has proven to negatively impact both cognitive development and overall academic success for K-12 students in Canada and the US, current scholastic outcomes indicate that our education systems continue to fail to fully meet the needs of our most socioeconomically disadvantaged students. Foresight for Every Kid builds upon evidence that students’ success in schools is in part reliant on their valuing the future, and that relationships to the future can be associated with socioeconomic status. Following an introduction to futures studies, there follows an analysis of overlaps between existing research on the impacts of poverty, contemporary teaching strategies for socioeconomically disadvantaged learners, neuroplasticity, and time perspective theory. The project then highlights the potential in futures studies to leverage further change in our school systems, specifically to grow the ‘future orientation’ of socioeconomically disadvantaged students in order to promote academic success. To that end, two ‘futurized’ tools for educators are introduced: a futurized educator profile, and a futurized teaching process framework.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".