Bibliographic record
Abstract
This thesis examines children’s (aged 11-12) ideas about climate change. Seventh grade children in 9 schools in Ontario were interviewed and submitted illustrated responses about climate change over a one year period of data collection. Qualitative grounded theory was used to allow themes from the data to emerge, and the use of computer software, NVivo7, was used to code and classify themes. The data were analyzed to answer three main research questions. First, the thesis explored if there were common similarities or differences between the children’s and adults’ responses (as gained from the literature). Second, children’s responses were grouped by geographical location. These locations included rural, urban, and suburban school. This was conducted in order to determine if any group differences exist among children in these three areas. The study found that children’s and adults perceptions are quite similar, and that in some situations, both groups tend to use substitution of other environmental knowledge (cultural models) in lieu of knowledge of climate change but that children also tended to use different cultural models to explain their ideas about climate change. The thesis concluded that no group differences existed among rural, urban, and suburban children and children in all groups tended to have much more detailed knowledge of mitigation strategies than the effects and causes of climate change. The thesis also concluded that a new educational framework, modeled after the Causes, Effects, and Mitigation Strategies of Climate Change (CEM Framework) ought to be used to redistribute this knowledge across these three areas.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".