Development and validation of MACK-12: A short multidimensional climate knowledge scale
Bibliographic record
Abstract
Accurate knowledge about climate change—including its causes, consequences, and solutions—plays a significant role in shaping people's pro-climate attitudes and behaviors. This knowledge influences voting behavior, policy support, personal lifestyle choices, and community-level actions, all contributing to society's collective response to climate change. However, few validated tools exist to assess people's climate knowledge, particularly short questionnaires suitable for large-scale studies of psychological constructs and behaviors related to the climate crisis. This research aimed to develop and validate a short, multidimensional climate knowledge scale—the Multidimensional Climate Knowledge Scale (MACK-12). In Study 1, we created and administered an initial set of 62 items to a representative sample of 2,000 adults in Quebec, Canada. These items covered various dimensions: greenhouse effect, causes and consequences of climate change, individual and collective solutions, and climate science. We selected twelve items with high psychometric quality for inclusion in the MACK-12, ensuring coverage of all targeted dimensions. We demonstrated the scale's validity and reliability using conventional metrics, including Cronbach's alpha and correlations between respondents' scores and education level. Study 2 confirmed MACK-12's test-retest reliability through a follow-up data collection (n = 500) two weeks later. Study 3 (n = 2,513) further demonstrated the scale's construct validity by showing that respondents' scores correlated with constructs known or expected to be associated with climate change knowledge (climate change denial, environmental concern, perceived urgency to act, and climate-friendly actions). This new climate knowledge scale can help researchers and decision-makers identify knowledge gaps among Quebecers and other populations worldwide, supporting more targeted communication strategies, policy design, and behavior-change campaigns to effectively engage the public in sustainable actions. The scale also offers valuable applications for interdisciplinary research: it can be integrated into large-scale observational studies alongside other measures assessing relevant concepts, such as personal values or political orientation.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".