Bibliographic record
Abstract
Mobile information systems (MISs) are having a major impact on businesses and individuals. No longer confined to the office or home, people can use devices that they carry with them, along with wireless communication networks, to access the systems and data that they need. In many cases MISs do not just replace traditional wired information systems or even provide similar functionality. Instead, they are planned, designed, and implemented with the unique characteristics of wireless communication and mobile client use in mind. These unique characteristics feature the need for specific design and development methodologies for MISs. Design methods allow considering systems independently of the existing information technologies, and thus enable the development of lasting solutions. Among the characteristics that a MIS design method needs to consider, we cite: unrestricted mobility of persons, scarcity of mobile devices’ power-source, and frequent disconnections of these devices. The field of MISs is the result of the convergence of high-speed wireless networks and personal mobile devices. The aim of MISs is to provide the ability to compute, communicate, and collaborate anywhere, anytime. Wireless technologies for communication are the link between mobile clients and other system components. Mobile client devices include various types, for example, mobile phones, personal digital assistants, and laptops. Samples of MIS applications are mobile commerce (Andreou et al., 2002), inventory systems in which stock clerks use special-purpose mobile devices to check inventory, police systems that allow officers to access criminal databases from laptops in their patrol cars, and tracking information systems with which truck drivers can check information on their loads, destinations, and revenues using mobile phones. MISs can be used in different domains and target different categories of people. In this article, we report on the rationale of having a method for designing and developing mobile information systems. This method includes a conceptual model, a set of requirements, and different steps for developing the system. The development of a method for MISs is an appropriate response to the need of professionals in the field of MISs. Indeed, this need is motivated by the increased demand that is emerging from multiple bodies: wireless service providers, wireless equipment manufacturers, companies developing applications over wireless systems, and businesses for which MISs are offered. Besides all these bodies, high-speed wireless data services are emerging (e.g., GPRS, UMTS), requiring some sort of new expertise. A design and development method for MISs should support professionals in their work.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".