Bibliographic record
Abstract
The motivation for this book came from years of observing computer science students at universities as well as professional programmers working in software development. I had come to the conclusion that there seemed to be a gap in their understanding of programming. They usually understood the syntax of the programming language they were using and had a reasonable grasp of such topics as algorithms and data structures. However, a program is not executed in a vacuum; it is executed in computer memory. This simple fact exerts a powerful influence on the actual behavior of the program - or, expressed more precisely, a subtle yet powerful influence on the semantics of the particular programming language. I had observed that many students and programmers did not fully understand how memory affected the behavior of the C and C++ programs they were designing. This book is an attempt to fill this gap and provide students and programmers alike with a text that is focused on this topic. In a typical computer science curriculum, it is expected that students take courses in computer architecture, operating systems, compilers, and principles of programming languages - courses that should provide them with a “model” of how memory matters in the behavior of programs. However, not all students end up taking all these courses, and even if they do, they may not take them in the right order. Often the courses are presented in a disjointed way, making it difficult for students to forge a unified view of how memory affects the execution of programs.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.362 | 0.250 |
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".