Anticipating Friction: The role of human rights in urban debates on migration and diversity: The case of Amsterdam, Hong Kong and Buenos Aires
Bibliographic record
Abstract
This research centres around the interaction between the city, and its actors, and human rights. In recent years, local governments more frequently collaborate with other actors, such as NGOs and international organisations, for the realisation of human rights; they apply and translate human rights norms directly in their local policies and legislation – in some cases independent of their national governments. The urban engagement with human rights, however, is not a linear process. It involves making choices on how to engage with human rights: which rights to focus on, how to understand human rights, what kind of activity to organise, for which target group and with whom to collaborate. Such choices are not made in a vacuum, because cities do not function as coherently operating actors, nor do the local governments ruling them. On the basis of fieldwork research on migration and diversity debates in three very dissimilar cities – Amsterdam, Hong Kong and Buenos Aires – this research assesses how the particularities of cities define what human rights can be.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".