Ethnographic Family Research: Predicaments and Possibilities of Doing Fieldwork on Intergenerational Relations in Singapore
Bibliographic record
Abstract
This paper addresses the use of ethnographic methods in the field known as family studies. While ethnographic methods are fundamental in the anthropological study of kinship and family, quantitative methods have long dominated family studies. To grasp the ideas and actions of human beings, however, we must pay attention to their intentions and recognize that intentions are not necessarily simply pragmatic reactions to economic or political conditions. One reward of ethnographic fieldwork is that the extended timeframe enables the researcher to gain insights into the construction of meaning in everyday life. Based on the author’s experiences during longterm fieldwork on intergenerational expectations and obligations in Singapore, the paper demonstrates how the role of the researcher, as well as the accumulation of ethnographic data, is shaped and constructed in interaction with informants. While an anthropological approach contributes to an in-depth understanding of family practices on the ground, it requires a high degree of self-reflexivity with regard to fieldwork relations and the interpretation of data, since the ethnographer’s social position, including age, gender, class, and ethnicity/nationality, inevitably shapes the access to informants. The paper sheds light on the different aspects of fieldwork and the process of ethnographic understanding.
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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 | 0.037 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.009 |
| 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".