Climate Autobiography Timeline: Adapting Timeline Research Methods to the Study of Climate Perceptions
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
Abstract Climate perception is a growing area of study in the social sciences and one that has implications on the tools and strategies we use to communicate climate change risk information. However, the range of climate perception studies remains limited, focused primarily on perceptions of day-to-day weather, sudden-onset severe events, or long-term permanent change. Phenomena situated between these extremes (e.g., annual- to decadal-scale variability) are largely missing from social science of climate research. Whether this is due to limited perception by research participants, is due to limited research attention, or is a reflection of the methods commonly applied to human dimensions of climate research, this gap precludes analysis of the full range of complex climate experiences and their influence on climate perception and understanding. In this paper, we offer a proof of concept for the climate autobiography timeline (CAT), a visual timeline tool developed to assess climate perception while prompting an ordered consideration of time, with the goal of eliciting insights into complex and long-term climate experiences such as low-frequency climate variability. Results are based off a preliminary application of the CAT across focus groups conducted in Newfoundland and Labrador, a province of Canada that is subject to low-frequency climate variability and frequent high-impact weather. Results reveal three key findings: 1) weather and climate narratives are commonly anchored to two time periods, potentially obscuring perceptions of variability; 2) narratives focus on socially important weather and climate phenomena; and 3) the social and visual coconstruction of weather and climate narratives may yield more holistic representations of local climate knowledge. Significance Statement The purpose of this work is to highlight the utility of timeline research methods to the study of climate perception research. Specifically, the climate autobiography timeline (CAT) serves as a tool that can address limitations of research tools commonly applied to the study of climate perceptions, notably the inability for current methods to elicit and organize complex climate experiences. Failure to capture these experiences may prevent a holistic and socially grounded understanding of climate perceptions. Drawing from a preliminary application of CATs in the province of Newfoundland and Labrador in Canada, we highlight how the tool can provide information complementary to, but distinct from, data collected through more commonly used methods such as interviews or surveys. This approach holds promise for analyses of long-term climate history, impacts of historical severe events, and cultural impact of weather and climate.
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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 | 0.016 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it