Inclusive circular economy: Promoting young adult's participation through citizen inquiry and creative participatory research methods in a developing country
Bibliographic record
Abstract
This study investigates the effectiveness of citizen inquiry and creative participatory methodologies in promoting inclusion and participation of children and young people in an underserved community in Nigeria in the global environmental issue of single-use plastics and the drive toward a plastic circular economy. It uses the principles guiding collaborative citizen science and inquiry-based learning to engage children and young people as co-researchers in a six-week participatory research project. The design employs a mixed-method approach that includes a pre-post-test, informal discussion, and focus group interviews with volunteers made up of eight children and sixteen young people to assess the methodology's effectiveness by determining changes in their knowledge, environmental attitude, and environmental behaviour. The results are analysed using descriptive statistics and paired t-tests to determine the relationship between the intervention and the variables measured. The summative assessment revealed no significant change in the young people's knowledge, but it did reveal an improvement in their environmental attitude and behaviour. The findings show that citizen inquiry has the potential to democratise global participation of children and young people in environmental issues through formal and informal settings, but designs must be tailored to the peculiar realities of the participants.
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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 | 0.030 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".