Toward Meaningfully Engaging Children in Futures Work
Bibliographic record
Abstract
Despite the fact that many decisions made today will affect our children’s tomorrows, there is a general bias that children and youth are incapable of understanding and discussing serious topics. As a result, we typically exclude them on matters of concern that we believe are beyond their capacity. This exclusion has been carried into civic participation wherein children are not given a voice in discussions that involve their current and future experiences as citizens. Foresight methods hold promise for developing skills to help us sense-make and vision in the complexity of today’s society. How might we engage children as participants in futures work? We conducted a literature review, consulted foresight practitioners who work with young people, and tested playshop prototypes engaging children in foresight methods and techniques. Findings show that although children and adults think differently, both views convey valuable meaning, and inviting all ages to the table can lead to more robust sense-making and visioning.
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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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".