In a ubiquitous world requirements are ubiquitous too
Bibliographic record
Abstract
Summary form only given. The soaring presence of devices that can sense the environment, human activity and social interactions in a ubiquitous fashion, opens the doors to potentially very effective multi-disciplinary research. Battery-powered tiny sensors can be distributed across an area to monitor conditions with very fine granularity. Moreover, mobile phones are powerful sensors that we voluntarily carry throughout our daily life. However, as well as introducing exciting opportunities, these technologies offer many challenges: writing software for these systems is all but obvious due to power, communication and computational constraints as well as to their context dynamicity. In addition, the interactions with the software users (i.e., the non computer scientists “scientists” collaborating in the projects) impose requirements that change the way in which software is conceived, tested and deployed. In this talk I will describe my experience and the lessons learned in two multi-disciplinary projects: collaboration with zoologists for animal monitoring through sensing and with social psychologists for monitoring human interactions through mobile phones. The talk will discuss the ubiquity of requirements when mobile and sensing start being employed.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".