From representational state transfer (REST) to representational action state transfer (REAST): enabling smarter web interactions for web tasking
Bibliographic record
Abstract
Today, the web is designed for information retrieval. Its current interaction model is web browsing. However, browsing is not an intuitive interaction model for web tasking. This paper provides a solution by introducing an architectural style called REpresentational Action State Transfer (REAST), extended from the classic architectural style of REpresentational State Transfer (REST). REAST represents the action state of the resource instead of the resource data state as in REST. By transferring the representation of action state of the resource between the requesting processer and the resource server in the architectural style of REAST, instead of transferring the data state of the resource as in the architectural style of REST, machine operated web interfaces with the dispensability of human web users is, for the first time, made possible. This opens up opportunities for new interaction model design that are better fit for web tasking where web browsing falls short. With its RESTful inheritance, REASTful resources share universal resource representation and the unified interface, thus have the architecturally built-in interoperability between enterprise resource items and items from the Internet of Things, in both machine automation and mixed initiatives between machine and human web users. The RESTful inheritance also means that REASTful resources relate in execution under the constraint of hypermedia controls. This enables the building of resource oriented intelligent web agents that can work generically across the web without domain specificity and can execute on actions that are non-preprogrammed. This paper coined such Resource Oriented Intelligent Agents 'the BOT'.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; both teacher heads agree on what is shown here.
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".