Bibliographic record
Abstract
Everyone wants to be happy, accepted by society and is concerned about the wellbeing of their family. Also, everyone realizes that while the economic resources are limited, there are no limitations of the needs of people. As a result, one may ask which needs are acceptable to address and what reasons are behind it. The evolution of science makes it easier for people to improve themselves, develop innovative technologies and increase the opportunities to satisfy their aspirations and needs. However, this does not apply to all people. It is no surprise that one starts questioning the effectiveness of the running economic systems or the ways resources are employed, as there are a number of social and environmental issues that one encounters today. In 2005, over a billion people faced the issue of the access to clean drinking water, while 2.6 billion people lived in unsanitary conditions. Approximately 3.2 million people die each year due to infectious diseases caused by water shortage. This number accounts for 6 per cent of all the deaths across the world. In 2008, there were around 1.4 billion people or, in other words, one-fifth of the world’s population living in extreme poverty. These kinds of issues are most common in Sub-Saharan Africa, as well as South and Southeast Asia. Whereas, the developed countries, with only a quarter of the world‘s total population, account for three-quarters of the total consumption and experience waste management problems. EU member states face problems of social exclusion and poverty as well. Approximately a quarter of the total EU’s population lives at-risk-of-relative poverty. In order to take care of the material well-being of their families, people have been leaving economically less developed countries for a few decades. These countries are left worse off by emigration. [...]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".