Call for papers
Bibliographic record
Abstract
Context-aware communication and computing have attracted increasing attention since it allows automatic adaptation of devices, systems, and applications to the changing user's context.The context is the information characterizing the situation of an entity and providing information about the present status of people, places, things and devices in the environment.An entity is a person, device, place, or object relevant to the interaction between a user and an application, such as location, time, activities, and services.Context awareness allows for customization or creation of the application to match the preferences of the individual user, based on current context such as enterprise environment or home network.A first area of interest concerns the Person Context Awareness.The recent emergence of the so-called social networks, the widespread presence of smartphones equipped by heterogeneous sensors, such as GPS receivers, accelerometers, compasses, microphones and cameras, and the availability of geo-referenced information enable analysis of new context definitions that may concern individual, social, and urban scenarios.Indeed, recently, the available information may include mobility patterns of people and also physical activities (movements), physical status, and emotional conditions.This information is often acquired and shared, in real time, by users.Allowing the reliable extraction and sharing of that information is a fundamental research issue with important applications.It could improve the experience of individual, communities, organizations, and societies by adapting context to the environment (home, hospitals, campuses, offices, etc.).
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.870 | 0.825 |
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; the direct Gemma label and the distilled Codex classifier 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".