Citizen science in K–12 school‐based learning settings
Bibliographic record
Abstract
Abstract Citizen science, or the public participation in scientific research, is a mechanism for student engagement, and co‐creation of knowledge in the scientific research process. Through participation in citizen science initiatives within school‐based learning environments, students can gain field experience, direct project scope, and contribute to broader research objectives while simultaneously achieving learning outcomes and fostering connections to their local communities. To capture the breadth and scope of existing citizen science initiatives applied in Kindergarten–Grade 12 schools, a systematic mapping exercise was undertaken to evaluate common themes related to the type of activities students participated in (i.e., the collection, transcription, categorization, and analysis of data), along with their level of participation in the citizen science initiatives (i.e., crowdsourcing, distributed intelligence, participatory science, and extreme citizen science). Of the 77 manuscripts extracted in the systematic map, nearly all (67/77) involved data collection, and a significant proportion of manuscripts captured a distributed intelligence level of participation (56/77).
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 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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".