How can we enable school students to learn and participate in science engagement initiatives? Roles and tasks of enablers
Bibliographic record
Abstract
Involving school students in authentic research beyond their school learning means creating participatory, out-of-school opportunities related to research processes, giving them a voice in the applied format of science engagement. Important for such endeavours is a group of people we identify as “enablers”. Based on insights from two long-term and large-scale science engagement initiatives in Germany (the Darwin Day science outreach and the Plastic Pirates citizen science program), we identified four principal work tasks of enablers. They are described as (i) aligning the needs, expectations and goals of involved participants, (ii) translating differing conceptions about science into shared visions, (iii) guiding the design of the initiative through educational theory, and (iv) evaluating the success of the out-of-school science engagement initiative. We further suggest that self-awareness of being an enabler, working at the interface of the research and education sphere, is an important prerequisite to successfully collaborate with 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 | 0.042 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".