Material and social remittances in highland Ecuador
Bibliographic record
Abstract
This thesis explores the material and non-material transfers from and to two rural settings in Andean Ecuador. Within the broad realm of the research on remittances, it explores in depth the type, nature, composition, uses, meanings, re-workings and negotiations of the transfers between migrant and non-migrant villagers. \n \nTwo villages in the Ecuadorian highland provinces of Azuay and Loja are the main research locations. These two provinces receive high remittance transfers, albeit from different origins: remittances to Azuay mainly originate in the US, whereas remittances to Loja are very likely to come from Spain and Italy. Due to very different socio-cultural features and different forms of migrants’ integration in the two destination areas, these two highland Ecuadorian provinces provide an excellent comparative context to research material and social remittances. Fieldwork was carried out in migrants’ villages of origin as well as in their new places of residence. This multi-sited ethnography was supported by a mixed-method approach involving a questionnaire (to gather information about material remittances), interviews (to shed light on social remittances) and participant observation (to provide the broader context for comprehending nuances in the data). \n \nThis research incorporates socio-demographic variables, such as gender, family structure and generation, in the analysis of material remittances. The relationship between remittance senders and receivers, that is usually overlooked, is regarded as a very important locus of power and negotiations. A refined typology of material remittances, taking into account remittance senders, receivers and non-receivers, is also provided. Finally, there is a microethnographic account of material remittances’ uses which problematizes over-simplistic pictures of remittance expenditure by embedding remittances into broader socio-cultural contexts. \n \nSurprisingly, given the large amount of academic work on remittances, there are still unexplored areas. Social remittances are one such area. By systematically researching social remittances, this thesis adds to the limited existing theoretical knowledge on social remittances, providing new information on their creation and content.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".