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 While social scientific studies have provided useful insights into the phenomenon of climate change, they, however, do not take a historical approach to the impacts of climate change, and people's perception of it. Historians have studied climate and its impact on the whole society but have neglected the everyday experiences and perceptions of climate change within a society such as ordinary people versus the elite perceptions, men versus women's experiences of climate change. Moreover, historians of climate have largely dealt with natural climate change in the distant past, but not with climate change caused by human activities. Since climate change that the world is witnessing in the past century is largely anthropogenic, historians therefore cannot neglect present‐day climate change and its impact on society. Furthermore, although climate change is a global environmental phenomenon, the poor and the marginalized social groups are vulnerable to the impacts of climate change more than others. Hence, climate change and the history of climate change needs to be understood from the perspective of these vulnerable groups in a society. I would, therefore, like to propose a new approach to doing history: people's history of climate change, which will be elaborated in this article.
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 | 0.000 | 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 | 0.000 | 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) | 0.011 | 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 it