Good Servants are Good Citizens: The Formation of the Filipino Servant Subject
Bibliographic record
Abstract
This dissertation explores the role of religion, culture, and politics in the formation of “Filipino servant subjectivity,” which revolves around practices of reciprocity, obligation, and sacrifice. Drawing from Foucault’s theory of governmentality, I argue that such subjectivity is a consequence of a person’s inculcation of cultural ethics that involve prioritizing one’s fellow human beings. To demonstrate how culture, religion, and politics in the Philippines overlap to form servant subjects, I focus on Couples for Christ (CFC), a Catholic charismatic group whose mission is to help renew the nation via the moral formation of each Filipino family. Operating both in the Philippines and in the diaspora, CFC promotes the Philippines’ moral and economic development by attempting to transform its members into moral subjects. In doing so, it aligns itself with the Catholic Church’s exhortation to the laity to actively contribute to the renewal of the Philippines through the formation of their social conscience, and with the Filipino government’s call for an active citizenship that entails the formation of resilient subjects. I also argue that CFC’s emphasis on the family is in accordance with the cultural concept of reciprocal indebtedness. Drawing on CFC’s moral manuals, seminars, and interviews with its members, I demonstrate that the family—which I interpret to be one’s closest kapwa— functions as a technology of subject formation, encouraging service and sacrifice for the good of its members. Among Filipino migrants in Canada where the policy of multiculturalism organizes society via a logic of difference, servant subjectivity is a currency of belonging that emphasizes their cultural difference, which in turn allows them to perform philanthropy in the Philippines via organizations such as CFC. Finally, I demonstrate the importance of Filipino cultural ethics in transforming beneficiaries of migrant philanthropy into moral subjects. Through the benevolence/gratitude binary, I show that the relationship between migrant philanthropists and Filipino beneficiaries parallels the civilizing mission of the United States to the Philippines in the twentieth century, a correspondence that I contend contributes to the rethinking of the aftermath of colonialism in the Philippines and elsewhere.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".