Bibliographic record
Abstract
While a growing body of research explores the ways in which NGOs affect IO accountability, relatively little high-level, sustained international advocacy has focused on the International Organization for Migration (IOM), and IOM has been under-examined in the literature on NGOs and IO accountability. This is surprising as IOM has a history of involvement in activities such as migrant returns and detention that may threaten or actively violate migrants’ rights—activities that call out for careful external scrutiny. This chapter explores the drivers and implications of this puzzling disconnect, and opportunities to overcome it. We map out the limited ways in which international human rights advocacy organizations have engaged with IOM, and consider why advocacy NGOs have not more actively pushed for increased accountability from IOM. International advocacy NGOs have important but still under-developed roles to play in advancing accountability for the human rights implications of IOM’s work. Enhancing accountability is a two-way street: there is a need for advocates to devote more attention to IOM, and develop more concerted advocacy strategies vis-à-vis IOM. Meanwhile, IOM should clearly recognize the importance of external advocacy, and engage more openly and systematically with human rights advocates, moving beyond traditional postures of defensiveness, dismissal and secrecy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".