Family and Collective Remittances to Mexico: A Multi‐dimensional Typology
Bibliographic record
Abstract
Abstract The development potential of remittances has resurfaced as a topic of analysis, based in part on dramatic increases in migration and amounts of money ‘sent home’, and partly in the growing interest and involvement by states and non‐state actors in gaining leverage over remittances. The trend is indicative of an emerging remittance‐based component of development and poverty reduction planning. This article uses the case of Mexico to make two broad arguments, one related to the importance of extra‐economic dimensions of remittances, particularly the social and political meanings of remittances, and the other based on a disaggregation of remittances into family, collective or community‐based, and investment remittances. Key dimensions of this typology include the constellation of remitters, receivers, and mediating institutions; the norms and logic(s) that regulate remittances; the uses of remittances (income versus savings); the social and political meanings of remittances; and the implications of such meanings for various interventions. The author concludes that policy and programme interventions need to recognize the specificity of each remittance type. Existing initiatives to bank the un‐banked and reduce transfer costs, for example, are effective for family remittances, but attempts to expand the share of remittances allocated to savings, or to turn community donations into profitable ventures, or small investments into large businesses, are much more complex and require a range of other interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".