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 pro-climate attitudes and behaviors, influencing voting behavior, policy support, personal lifestyle choices, and community-level actions. However, few validated tools exist to assess climate knowledge, particularly short questionnaires suitable for large-scale studies of psychological constructs and behaviors related to the climate crisis. This research addressed this gap in two ways. First, we developed and validated a short, multidimensional climate knowledge scale specific to Quebec: the 12-item Multidimensional Assessment of Climate Knowledge—Quebec version (MACK-12-QC). In Study 1, an initial set of 62 items covering greenhouse effect, causes and consequences of climate change, individual and collective solutions, and climate science was administrated to a representative sample of 2,000 adults in Quebec, Canada. Twelve items with high psychometric quality were selected for the final scale, ensuring coverage of all targeted dimensions. We demonstrated its reliability and validity using conventional metrics (e.g. Cronbach’s alpha, correlation with education level). Study 2 ( n = 502) confirmed test-retest reliability and Study 3 ( n = 2,513) demonstrated construct validity, showing correlations 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). Second, to explore broader applicability, we proposed a general version of the scale, the MACK-12, replacing Quebec-specific items with more universal content. This scale can be used to assess climate knowledge across different populations, helping researchers and decision-makers identify knowledge gaps and design targeted communication strategies, policies, and behavior-change interventions. Its short, multidimensional format also makes it suitable for integration into large-scale observational studies alongside other psychological or sociopolitical measures.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".