Orca: A Single-Language Web Framework for Collaborative Development
Bibliographic record
Abstract
In the last few years, the Web has been established as a platform for interactive applications. However, creating Web applications involves numerous challenges since the Web has been created to serve static content. In particular, the separation of the client- and the server-side, being only connected through the unidirectional Hypertext Transfer Protocol, forces developers to apply two programming languages including different libraries, conventions, and tools. Developers create expert knowledge by specializing on a few of all involved technologies. Consequently, the diverse knowledge of team members makes collaboration in Web development laboriously. We present the Orca framework that allows developers to work collaboratively on client-server applications in a single object-oriented programming language. Based on the Smalltalk programming language, full access to existing libraries, and a bidirectional messaging abstraction, Orca provides a consistent environment that supports common idioms and patterns in client- and server-side code. It reduces expert knowledge and the number of development tools and, thus, facilitates the collaboration of Web developers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".