Effectiveness of citizen inquiry approach in promoting environmental knowledge of young people
Bibliographic record
Abstract
The unsustainable and unsafe disposal of single-use plastics poses a major environmental risk to society. Although there have been global calls to transition from a linear economy to a circular economy for plastics, the participation of young people across communities is critical to the achievement of this goal. To advocate for the circular economy in their community, young people must be empowered with adequate knowledge about single-use plastics and the circular plastic economy. Therefore, this study investigates the effectiveness of the citizen inquiry approach in enhancing young people’s knowledge of issues concerning single-use plastics within the context of the circular economy. It situates the conceptual underpinnings of citizen inquiry within the framework of collaborative citizen science, fused with inquiry-based learning, and makes a concerted effort for participants to be coresearchers in the design, implementation, and knowledge creation in six-week quasi-experimental research. The study utilizes a quantitative data collection strategy, providing a pretest before the study and a posttest after the study using the same instrument to assess the change in participants’ procedural and declarative knowledge. The data is analyzed using descriptive and t-test statistics to evaluate the difference in means between test scores before and after the intervention. The t-test results reveal a statistically significant difference in the procedural knowledge (t(4) = 4.355, p = 0.006), the declarative knowledge (t(18) = 10.762, p < 0.001), and the overall knowledge (t(23) = 11.688, p < 0.001) of the participants as measured by the pre-posttest. The results demonstrated a considerable improvement in the participants’ procedural, declarative, and overall knowledge. The findings indicate that the citizen inquiry technique can democratize learning and the research process, but it requires the design to be contextualized.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".