Bibliographic record
Abstract
The following independent study seeks to research specific countries that are known to have high amounts of child labour such as Bangladesh, Ethiopia and Ghana as a form of compiled case studies. The different countries were selected because of the different industries of child labour that they are involved in such as textiles, mining and chocolate farming. Information will be gathered through a variety of reliable sources and scholarly reports. Due to the broad scope of this topic, investigation will also be done in the gender disparities and the role globalized supply chains play in the issue to determine their significance. This topic will be explored through the constructivist framework through the Norm Cycle Theory as to how these practices have become endorsed in these developing countries. By attaining the knowledge on the major contributions to child labour, examinations will be given to how Covid-19 regresses the progress that has been made to combat against child labour. All in all, this topic is crucial for it is a direct indicator of the progress a country is making towards development. Furthermore, child labour is a demand on the human rights of these children who have had their opportunities stolen from them. Department: Political Science Faculty Mentor: Dr. Chaldeans Mensah
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".