Bibliographic record
Abstract
This article aims to demonstrate that despite the international community’s best efforts to eradicate slavery and slavery-like practices, such as forced labour, these phenomenons are still on the rise today. It will be shown that sweatshop conditions, in the worst of cases, fit the definition of modern forms of slavery and slavery-like practices. Moreover, it will be demonstrated that voluntary measures adopted by multinational corporations are insufficient and more coercive measures need to be taken. Indeed, as submitting workers to sweatshop conditions can amount to the committing of an international crime, corporations and Corporate Executive Officers engaging in these practices should be prosecuted for doing so. This article seeks to demonstrate that the eradication of sweatshops could be achieved by using concepts developed by international criminal law. Additionally, other countries could adopt national measures (like the U.S.A.’s ATCA and RICO) in order to avoid problems raised by corporate structure, as well as adequately compensate the victims of sweatshop labour.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".