Bibliographic record
Abstract
Une bonne d'enfant des Philippines pleure la perte de ses enfants qu'elle a du laisser chez elle pour travailler aux Etats-Unis afin de leur procurer le nicessaire Elle remarque le contraste entre sa vie et celle de la femme dont elle garde les enfants et qu'elle est arrivee a aimer. My name is Diwata Navarro. I am 29 years old and I am from the town of Abaga in the Philippines. Since I was a child, I have learned that there is one marker that has set out my entire path in life. This thing that 1 carry around with me is often seen as an uncontrollable curse that places me at the bottom of the pyramid in all aspects of life. It makes me a target of discrimination, of inequality and of subordination. I am a woman, a description that tends to make the harsh realities of my situation acceptable to the world around me. When I was twenty, I was married to a man who I thought loved me very much. He was sneaky; at least going into it he was. Helping me clean dishes, cooking every now and then, my husband even told me once that I should find a hobby and set aside a little time for myself every day. I thought I was lucky, but after our first child came I realized he was just like the other men in my town and my hopes for a marriage where both partners were equal quickly left my mind. I cared for both the children and home while my husband spent all of his paycheques on booze and cigarettes. About eight months into my fourth pregnancy, the constant debt we were in caused my husband to move on and leave my children and myself with nothing. I was pregnant and alone with three other mouths to feed. I felt ashamed. What kind of parent was I? We had no money and I couldn't work; I was lost and afraid. My sister who lived in a village a few miles from my home in Abaga sent us the little money she could spare, but we were both aware that it would not be enough to keep my family alive. Two months after my fourth child was born, I looked at my babies, kissed their foreheads, and left on a boat to the United States. I can still remember the tears in the eyes of my eldest, who was only five years old at the time, and the sickening pain I felt in my stomach as I left them with my neighbour who was my only option until I could make enough money to send them to my sister's home. Once I arrived in the States I stayed with my sisters brother-in-law who had a home in Chicago. It took me two months to secure a position as a fulltime nanny at the home of Mr. and Mrs. Tayard. That was six years ago and I have not seen my children since. My sister likes to comfort me by saying I did what I needed to do for my family, but the truth is I no longer feel like I deserve the title of being a mother. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.469 | 0.242 |
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".