Bibliographic record
Abstract
Writing in his encyclical Laudato Si’: Care for Our Common Home, Pope Francis asserts that there is a “solid scientific consensus” on the reality of climate change and on its origins in human activities (#23). Unfortunately, much of the detailed scientific understanding that underlies this claim has not been made readily accessible to scholars in fields outside of the sciences. This paper aims to correct that omission by examining the science of climate change in the context of integral ecology. In this light, it will be demonstrated how human activities cause climate change, climate change has devastating effects on human lives and livelihoods, and human actions are necessary in order to mitigate climate change. Mitigating climate change requires societal actions that bring together technological, cultural, sociological, economic, and political considerations. The question is, do we have the wisdom to see that we are the cause of climate change, that climate change is rapidly making our planet unlivable for large numbers of human beings, and that we have to take strong and immediate actions if we are to avoid ever worsening future disasters? Hopefully, attention to integral ecology—to the interplay of human activities with natural ecosystems—can encourage us to do so.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| 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".