Aspectual Decomposition of Transactions
Bibliographic record
Abstract
It is a pleasure to thank the many people who made this thesis possible. Firstly, I would like to thank my supervisor, Jörg Kienzle, the director of the Software Engineering Lab at McGill University. I could not have imagined having a better advisor for my M.Sc. Throughout my thesis, he provided encouragement, good teaching and lots of good ideas. I would have definitely been lost without him. Very special thanks go to Meaghan Worth for giving me the extra strength, motivation and love necessary to get things done. Writing this thesis would have been extremely difficult without her and Cessna’s presence. Lastly, and most importantly, I wish to thank my family, my mother Eser Bölükba¸sı, my father Nejat Bölükba¸sı, and my sister Gamze Bölükba¸sı. They raised me, supported me, taught me, and loved me. To them I dedicate this thesis. i The AspectOptima project aims to build an aspect-oriented framework that pro-vides run-time support for transactions. The previously established decomposition
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".