Home, Family and Climate Change: Understanding the power of home to transform societies in the face of Climate Emergency
Bibliographic record
Abstract
In the face of a global emergency, the home and family instantly and instinctively emerged as the resilient unit on which our society can rely and through which we can adapt and reset our systems and global operations; lessons learned, adaptably and flexibility were tested, and it worked. Why, therefore, we look elsewhere to build resilience and response mechanism to combat Climate Change. This report explores the role home and family play in the transition towards a sustainable and carbon- neutral planet, where our Carbon footprint is neutralised by offsetting our consumption with the production of clean energy and a sustainable lifestyle. Building on the lessons learnt during COVID-19 Pandemic, this report brings together collective effort of experts, scholars and scientists from diverse disciplines, professions, and research backgrounds to debate the challenges and opportunities facing the home as societal institutions to achieve that goal. It tries to respond to a key question, ‘how can we engage more effectively with the home and family as a resilient unit to help societies and economies combat Climate Change?’"
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".