Bibliographic record
Abstract
“Saying thank you is more than good manners. It is good spirituality.” – Alfred Painter. I thank God for everything, including my own self. Words fall short here. With immense gratitude, I acknowledge my advisors, Professors Divy Agrawal and Amr El Abbadi, for providing continuous support, guidance, mentoring, and technical advise over the course of my doctoral study. I still remember the day in the Winter quarter of 2007 when I met Divy and Amr discussing the possibilities of joining their research group. I had barely competed my first course on Distributed Systems and had superficial knowledge of Database Systems. On that day, I never imagined that four years down the line, I will be writing my dissertation on a topic that marries these two research areas. Divy and Amr’s research insights have made this dissertation possible. I am fortunate to have been mentored by Divy and Amr, both of whom have been awarded the prestigious Outstanding Graduate Mentor Award by UCSB’s Senate. Besides their great guidance on the technical side, I will never forget their kind fatherly and friendly attitude. Their characters will continue to inspire me.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".