Bibliographic record
Abstract
A growing body of publications is available on young carers and young adult carers, mostly from the United Kingdom, but an increasing numbers of refereed journal articles from Australia, Europe, and a few other countries, including the African continent, are now discussing the issue of young carers. Becker 2007 provides the only article, as of the early 21st century, that looks at young carers in cross-national perspective and reviews the level of awareness in different countries, suggesting reasons why there is considerable variation but also some overlap in country responses. As of the early 21st century, only a couple of textbooks are available on young carers because the field is relatively new and very few academics are heavily engaged in researching in this area. Nonetheless, the earliest textbook by Becker, et al. 1998 deserves to be examined as many of the themes within it have been replicated over the last fifteen years by other researchers and practitioners. Most publications in this field are refereed journal articles, but a significant number of shorter items have been published in practitioner journals. Such practitioner articles are not cited here because they are often anecdotal in nature and, in most cases, have not been subjected to rigorous peer review.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.116 | 0.034 |
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".