Bibliographic record
Abstract
T he java developers' slogan is “100% Java,” meaning “Don't taint your Java software by incorporating non-Java components.” Yet, Java itself is neither 100% pure object nor 100% pure object-oriented; it is “tainted” with components of the procedural programming paradigm. The Java designers have borrowed many ideas from diverse sources—other object-oriented languages, especially C++ and Smalltalk, design patterns (see Gamma, E. et al. Design Patterns: Elements of Reusable Object-Oriented Software , Addison-Wesley, 1995), and basic object-oriented design principles—picking and choosing good ideas from each. And they've done a truly fine job. However, at one particular juncture, I believe the language architects made the wrong choice, and that was the decision to incorporate non-object primitive types into the otherwise uniform object-oriented language model. There are two types of types in Java. By primitive types, I mean elementary variable types such as int, boolean, float, long, short, and so on. These are sometimes referred to as “built-in” types. The other type of type is objects: a variable can be declared to be of type Object or String or any other class. Referring to the two types of types as metatypes , we have primitive and object metatypes (the truth is, the two metatypes are primitive and reference, which includes not only objects, but interfaces and arrays—more on why they are “reference” types later). By incorporating both metatypes, Java is not populated purely with objects.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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".