Bibliographic record
Abstract
Variables as “data containers” with names. Values as data - simple (innate or elementary) data, structures, and objects. Referencing variables through pointers. Unnamed “data containers” and their referencing through pointers. The dual role of pointers as address holders and binary code “interpreters”. Various interpretations of the contents of a piece of memory. Pointer arithmetic. Why C/C++ cannot be interpreted in a platform-free manner like Java can. Why C/C++ cannot have a garbage collector. During the execution of a program, a variable of the program corresponds to a location in memory, and the address of that location replaces all symbolic references to the variable in the load module. This is one of the important facts touched upon in Chapter 2 when we discussed why we can behave as if the program in its source form executes in the memory. In this chapter we will refine this notion and discuss its consequences. The idea of variable as “data container” is very natural. In its crudest form we can imagine a variable to be a box, and whatever is in the box is the value of that variable. If we want to evaluate the variable (i.e., find its value), all we need do is look in the box and see what is in there; when we want to store something in the variable, we simply put it into the box. In fact, this crude notion is not that far from reality.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.023 |
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".