Bibliographic record
Abstract
M oving to a new language involves some paradigm shifts in how you think about structuring programs and solving problems. Often we think in terms of solutions instead of problems and so ask questions like, “How do I pass a method pointer Java?” instead of “How do I encapsulate a behavior reference in Java?” This is particularly important for C++ programmers migrating to Java, because the similarity in syntax between the languages can lead to the assumption that the language paradigm is identical. I'll discuss two classes of problems faced by developers. The first is what I call “Conceptual Confusion,” where a user of one language carries their assumptions to another language and then gets confused when their assumptions are invalid. The second class is the desire for new features to be added to the language. This “Creeping Featurism” generally involves adding complexity to the language for the sake of mimicking another language's feature, often no more than syntactic sugar. That is, the proposed feature may reduce the typing without adding to the power of the language. Let me warn you of my bias: I find that too many features in a language confuse me. I find that a simple language based on a single paradigm provides for less confusion, better maintainability, and quicker code development. You may understand that neat “constant reference” feature, but think of the person who may have to fix, reuse, or extend your code a year from now.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.055 |
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".