Polymorphic panelists
Bibliographic record
Abstract
Polymorphism is an important object-oriented programming concept in which objects from two or more different classes respond to the same set of messages. For instance, HourlyEmployee, SalariedEmployee, and ContractEmployee all respond to the message calculatePay(). Instances of each class "do the right thing" to calculate their pay even though the methods to do so may be quite different. But the payroll program using these classes doesn't care - it can ask each object for the amount owed without caring what kind of employee it represents or how the amount is calculated.The panelists are all instances of subclasses of Professor which will respond to the following queries. Since each of the subclasses implement these queries differently, the answers will usually be different as well!• polymorphPreconditions(): The object (professor) specifies the information students must know before polymorphism is introduced in their class.• polymorphPresentation(): The object (professor) describes how polymorphism is introduced in their class.• polymorphStudentUsage(): The object (professor) describes how their students use polymorphism later in the course.• answerQuestions(): The object (professor) responds to any questions about their approach.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.338 | 0.163 |
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".