Bibliographic record
Abstract
As the Western world continues to progress to new heights of development, the emphasis on the care and rehabilitation of children living with physical and mental disabilities becomes more focused. In schools across the developed world, clubs and societies are formed to promote the incorporation of students with disabilities into our friend groups and ultimately our lives as well. The vast majority of education and social institutes have a zero tolerance policy concerning discrimination against children with mental and physical challenges. Hundreds of charities, companies, and organizations raise funds and awareness in support of this cause. We have an unmistakably beautiful word for these children in our society: special. And that is truly what they are. However, this is not the case for the vast majority of the world’s population. In cultures where ‘different’ is commonly associated with ‘unwanted’, children with mental and physical disabilities are abandoned, neglected, and tossed aside. In Equatorial Guinea, over 77% of the population live in extreme poverty, barely managing to take care of themselves day to day. As a result, children with special needs are disregarded as an extra burden to their families and to their society. How is it that something that comes to define the word special in certain places can be the same thing that makes children undesirable in others? Perhaps the answer lies in the environmental, social and economical factors in which the child is found.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".