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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".