Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract

 
 
 As the globe continues to experience the effects of climate change, researchers must continue to investigate factors that contribute to individuals' attitudes concerning climate change. This study utilizes survey data from 1,539 Canadians gathered in 2019. The data was analyzed using ordinary least square linear regression to analyze how political ideology, gender, and level of education correlate with individuals’ level of environmental concern. Approximately 83.2% of Canadians rated themselves as having a moderate level of environmental concern or higher in the collected survey data, suggesting that most Canadians express some amount of environmental concern. Canadians with a conservative political ideology have a lower level of environmental concern than their liberal counterparts. Within the Canadian context, there is no statistically significant relationship between level of education and concern for the environment. Females are more concerned, on average, about the environment, compared to males. Canadians’ gender identity seems to influence their level of environmental concern. However, more representation of non-binary individuals is needed in future data-gathering to analyze non-binary individuals' level of environmental concern. The paper further discusses these variables' effects on the level of environmental concern.
 
 
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.021 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it