Bibliographic record
Abstract
As children grow older, their birthdays not only mark the completion of another year of life, but can also deem them legally able to participate in certain aspects of society, such as voting or driving, according to Ontario’s age-based laws. In this project, I explore the views and perspectives of four children, aged 12-15, on some of the age-based laws in Ontario. In order to inform my research on children’s perspectives toward age-based laws, I have drawn on texts that discuss the origin of these laws, and differing perspectives toward the chosen ages for certain laws. Through informal interviews and discussions, I have gained an understanding as to whether the participants view these laws as a form of protection, or a denial of their participation in society, if a particular age-based law has more relevance to the participants, and whether they feel certain legal ages should be reassessed. The collected data have been analyzed using articles in the United Nations Convention on the Rights of the Child, in order to assess whether the participants feel age-based laws are a way for the government to protect or deny children of their rights. Ontario’s age-based laws may positively or negatively implicate the lives of children, and it is vital that such implications are analyzed through research with children.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".