Bibliographic record
Abstract
O bjects are good . Patterns are good. Networks are good. These statements have almost become IT axioms in the last few years. I'm not going to buck the trend here. Objects are indeed good. Networks and patterns help to make them even better. I'm going to show a relatively easy way to link these three things together. Well designed object-oriented applications share a common topology (see Riel, A. Object-Oriented Design Heuristics . Addison-Wesley, 1996.) By topology I mean the natural features of an entity and their structural relationships. A well-planned object model will spread out the application logic within a system. Related data and behavior will be kept in logical, isolated units. Structuring applications in this way promotes reuse, facilitates comprehension, and minimizes the effects of change. One side effect of partitioning a system in this way is the need for these “logical isolated units” to communicate with each other. Using controller classes to organize the communication is not a viable solution because it concentrates the system's intelligence in a single place. How can we share information without cluttering our design? The Observer pattern (see Gamma, E., R. Helm, R. Johnson, and J. Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software . Addison-Wesley, 1995.) provides a possible answer to our question. In the applicability section of the pattern description we find that Observer is a good way to “notify other objects without making assumptions about who these objects are.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".