Bibliographic record
Abstract
This Chapter adopts an idealistic perspective to discuss the UN’s Sustainable Development Goals (SDGs) dealing with two questions: (1) How can these noble goals be financed? and (2) Where to start implementation? It is argued that SDGs cannot be achieved without provision for their financing. An ideal world is a just world without poverty and hunger, with peace and security, there is human dignity for all, with access to health, education, housing, and all other basic human needs. In such an ideal world, every individual counts. There is entitlement to a universal basic income (UBI). If cash payment is not feasible for political or economic reasons, an equivalent investment in creating a just and sustainable world should be considered. Our global ecosystem is an intricately balanced global public good (GPG) for the collective benefit of humanity. Diverse species in forests, seas and on land are inter-dependent, co-existing in a delicate support system. Rain, sunshine and labor support agriculture and food production, industry provide goods and services, while good governance ensures health, safety, and other basic human needs. It all fits together like a complex machine. When a single component is damaged, the whole malfunctions. Poverty and Climate change are the top man-made damage in our world today. Sustainability, a global strategy to keep the system in good order, is now a global challenge.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.018 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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".