Youth Participation to Solve Relevant, Real-World Sustainability Challenges Using Create2Solve DIY STEAM Kits: My Reflexive Account
Bibliographic record
Abstract
Young people have the potential to be an investment in their communities, potentially leading solutions to wicked sustainability challenges in developing countries. Still, extant literature highlights that youth of color from low-income neighborhoods are: a) greatly excluded from sustainable development initiatives, and/or b) engaged in learning content that is irrelevant to them. Such deficit-based, tokenistic and alienated learning activities constrict youth engagement and participation, particularly in innovative 21st century learning paradigms, such as science, technology, engineering, arts and mathematics (STEAM) education. In this paper, I present ‘Create2Solve do-it-yourself (DIY) STEAM Kits’, a project developed in collaboration with a non-profit school in at-risk community. The Project is a community- and learner-relevant project which adopts an asset-based, non-tokenistic lens to view and position young people as civic citizens who are both capable and motivated to ‘do STEAM’ for community transformation. Particularly, I offer a reflexive account of my experience of designing and implementing Create2Solve Kits as I worked closely with youth. Towards the end, I present an evaluation of key takeaways learned from my experiences of facilitating this process. Through my reflective analysis, I propose that youth are keen to participate in sustainable development projects, provided that such projects: a) position them as capable and resourceful citizens; b) remain relevant to their local community contexts while helping them improve understanding of complex STEAM concepts; c) allow them to substantially contribute to their personal, community, and societal transformation; and d) foster non-tokenistic participation where their input and decisions drive the work.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".