Bibliographic record
Abstract
Key architectural elements of the web, namely, HTTP, URL and HTML enable a very simple user model of the web based on hyperlinks. While this model allows browser-based access to a wide array of online content and resources, the limitations in user experience provided in this interaction model are increasingly apparent. Two decades after the birth of the web, new technologies such as Rich Internet Application, AJAX, and Web 2.0 seek to improve web user interfaces, but in general their main benefit is to individual server sites. Little advancement has been made to advance the user model of the web at a macro level where the interaction is driven not by the server but by the user. This paper proposes a novel approach to scientific study of the Web (Web science) where the traditional relationship between users and servers is inverted, so that web services are configured and integrated across multiple servers/sites in order to address the needs of users. The resulting interaction paradigm is referred to here as smart interaction. The Smart interaction approach is quite different from the current hyperlink-oriented user model driven from the perspective of the server side. Smart interactions require new web infrastructure (e.g., runtime components) and new patterns of services and resource interactions and compositions. A Complementary area of research is smart services; where the focus is on abstracting these web infrastructures and service interaction patterns into appropriate web models and algorithms. The combination of smart interaction and smart services will then result in a smart internet where user experience is enhanced, and user productivity unleashed, by passing control back to users.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".