Bibliographic record
Abstract
Abstract Climate change represents an urgent and potentially irreversible threat to human societies, economies, and the planet. Yet despite clear signals, we are slow to act in a meaningful way, despite the fact that we have the legal, political, and technological tools to transition our economies to net zero carbon. While some businesses are reluctant to take significant steps to reduce their carbon footprint, many companies are well-intentioned but feel somewhat paralysed in the face of overwhelming data that portend a financially and environmentally devastating future. Yet we can still reverse the trajectory of climate change, but it requires bold and informed action to reduce our carbon footprint in a manner that embeds fairness in the transition. This book offers a guide for companies, pension funds, asset managers, and other institutional investors to commence the legal, governance, and financial strategies needed for effective climate mitigation and adaptation, and to help distribute the economic benefits of these actions to their stakeholders. It takes the reader from ideas to action, from first steps to a more meaningful contribution to the move towards a ‘climate positive’ circular economy. It can also serve as a helpful guide to everyone implicated in a corporation’s activities—employees, pensioners, consumers, banks and other lenders, policy-makers, and community members. It offers insights into what we should be expecting, and asking, of these individuals who have taken responsibility for effectively managing our savings, our retirement funds, our investments, and our tax dollars.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".