Bibliographic record
Abstract
Preferential trade agreements have become common ways to protect or restrict access to national markets in products and services. The United States has signed trade agreements with almost two dozen countries as close as Mexico and Canada and as distant as Morocco and Australia. The European Union has done the same. In addition to addressing economic issues, these agreements also regulate the protection of human rights. In Forced to Be Good , Emilie M. Hafner-Burton tells the story of the politics of such agreements and of the ways in which governments pursue market integration policies that advance their own political interests, including human rights.How and why do global norms for social justice become international regulations linked to seemingly unrelated issues, such as trade? Hafner-Burton finds that the process has been unconventional. Efforts by human rights advocates and labor unions to spread human rights ideals, for example, do not explain why American and European governments employ preferential trade agreements to protect human rights. Instead, most of the regulations protecting human rights are codified in global moral principles and laws only because they serve policymakers' interests in accumulating power or resources or solving other problems. Otherwise, demands by moral advocates are tossed aside. And, as Hafner-Burton shows, even the inclusion of human rights protections in trade agreements is no guarantee of real change, because many of the governments that sign on to fair trade regulations oppose such protections and do not intend to force their implementation.Ultimately, Hafner-Burton finds that, despite the difficulty of enforcing good regulations and the less-than-noble motives for including them, trade agreements that include human rights provisions have made a positive difference in the lives of some of the people they are intended-on paper, at least-to protect.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".