Inclusive counting: an essential but insufficient approach to account for missing migrants in Panama and Colombia’s shared Darien Gap
Bibliographic record
Abstract
International organisations increasingly call for, and support efforts to gather, more comprehensive data on missing migrants incidents. This article explores current practices of counting missing migrants, focusing in particular on Panama and Colombia’s shared Darien Gap. Drawing on a novel database of some missing migrants cases in the Darien, I demonstrate how insufficient counting practices can still provide essential information on the missing. The article examines, first, how detailing the demographic and event patterns of known missing migrants incidents can assist in estimating what may have happened to others travelling on the same migration pathway. Second, the article demonstrates how current institutional enumeration practices privilege state actors’ knowledge while failing to account for other forms of crucial knowledge on missing migrants held by non-institutional actors. To remedy these challenges, this article incorporates information held by migrants’ families and travel acquaintances on the missing together with institutional information. Ultimately, this article argues that by implementing a more inclusive counting approach for missing migrants, we can more explicitly begin accounting for the missing in the Darien Gap.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".