Bibliographic record
Abstract
It seems really difficult to do things that you have no interest in. I would say it’s a bit difficult but not impossible to do. It’s a sacrifice that you sometimes have to give for your loved ones. This is exactly the same situation that is happening to me right now or is probably going to happen. This story began when I was a kid. I used to be a bad child who would break his toys to see what was inside. During my childhood, I loved to repair my toys, my bicycle, my motorbike, and even small electronic objects like radios, watches, etc. It didn’t really matter if I was able to repair all of them. passion was only to look at their mechanisms. One of my father’s friends was a doctor who visited my house regularly. He was a nice person and maybe that was why my mother seemed to be so inspired by him. When I was in eighth grade, I realized that I was developing an interest in engineering. However, that was my passion and ambition, but my mother had something else in mind for me. I realized this when she told me one day. My son if you become a doctor that will be the biggest joy of my life. I was a bit confused at the time because I had no interest in medicine. It troubled me because my mother was the most important person in my life, and I did not want to deny her feelings. Afterwards, some personal problems arose in my family. Financial problems were the most important out of all of them. This was during the time when my father left to Canada. mother worked really hard to take care of my siblings and me. I thought that I was selfish because I was only thinking about myself. I then changed my mind.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.326 | 0.146 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".