Bibliographic record
Abstract
The inhabitants of Western countries happily consume products from around the world. They drink coffee from Ethiopia, eat bananas from Columbia, use computers from China, wear clothes from Cambodia, walk on carpets from Pakistan, talk to customer service representatives in India, display flowers from Kenya and show off diamonds from Africa. These patterns of trade and consumption influence working conditions in both importing and exporting countries. When Western industries need cheap labour for domestic production and service provision, they often turn from importing products to importing people. Examples include the UK importing health workers from Africa, the US relying on Mexican labour for agricultural and service work, Canada’s importation of Filipinas to work as nannies, Arab Gulf states importing Indian construction workers and Western Europe importing East European women to work in the sex trade. This movement of workers also affects labour conditions in both the importing and exporting countries. As products and services move across borders, labour issues and policies increasingly have a transnational impact. New patterns of consumption, production and movement have internationalised labour policy. Whereas domestic regulation of working conditions was seen to be sufficient in an era when most production and consumption was nationally based, increasing global exchanges reduce the influence of such regulation. Because of the globalisation of communication, the conditions of work generated byglobal production and exchange have also appeared in public debate and generated political pressure for action. Thus, stories of child labour in the Pakistan carpet industry, forced labour in the West African diamond industry, sweatshop labour in China, dangerous and exploitative working conditions for migrants and undocumented workers in the US and European Union (EU) have generated demands for new forms of regulation of working conditions on a transnational basis.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.192 | 0.086 |
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".