Children Challenging Industry: improving young pupils’ engagement with science through links with industry
Bibliographic record
Abstract
Perceptions of science, industry and related careers are not well-understood in children below age 11, with many potentially influential factors needing research. The Centre for Industry Education Collaboration at the University of York has run the Children Challenging Industry programme (CCI) since 1996. The CCI consists of components designed to place curriculum science in a real-world context, aiming to improve knowledge about and attitudes towards STEM-focused industry, pupils’ STEM career aspirations and attitudes towards science. Professional development for teachers and training for industry partners are also important elements of the CCI, but they are not the focus here. In this evaluative study, 508 children aged 9-11 from 23 English primary schools in two UK areas, completed pre- and post-intervention online questionnaires in the academic year 2019-2020 to gather quantitative and qualitative data on their perceptions. The quantitative data were compared across different groups and time points, and qualitative data was explored thematically. The data suggest that the CCI generally positively increased children's experiences of science learning, particularly where baseline awareness of industry was low. The outcomes showed that the positive impact of the CCI on attitudes towards science, and knowledge of industry, including STEM careers in industry, was statistically significant.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".