Adapting REST To REAST, Building Smarter Interactions for Personal Web Tasking
Bibliographic record
Abstract
REpresentational State Transfer (REST) today represents and transfers the data-states of distrib-uted resources. Through hypermedia-based inter-actions among these representations and transfers, the web's original goal of information retrieval is accomplished. However, despite of the fact that the web today has evolved beyond information retrieval into task executions, the original web interaction model built for information browsing has not been enhanced accordingly. This paper proposes a hypermedia-based RESTful model for task expression, delegation and execution through the representations and transfers of action-states of distributed resources, termed as REpresentational Action State Transfer (REAST). The con-tributions of this paper are (1) a representation of hypermedia-based task expressions that enables the building of user-controlled interactive apps, (2) a technique for Resource Oriented Web Auto-mation (ROWA) using a dedicated media type designed for machine processing as well as ma-chine-initiated and machine-executed task expressions within the RESTful HATEOAS constraints, (3) REST-based Resource Oriented Intelligent Agents (ROIA) to act on users' behalf across the web without domain-specificity, and (4) an in-teroperability model for tasks execution involving diversified resources types (e.g., enterprise re-sources and internet of things) working seamlessly together within the RESTful architecture.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".