Toward a Framework for the Discovery and Acquisition of Mobile Applications
Bibliographic record
Abstract
Mobile application development is a field that has gone through rapid change during the past two decades. Although, in the past, the development and control of mobile devices was performed by phone manufacturers and network carriers, and development was done ‘in house’, the evolution of mobile device capabilities has expanded the potential for mobile application developers. This evolution has created four main actors in the world of mobile applications: the network carrier, the phone manufacturer, the service provider, and the application developer. The application developer must run through a gauntlet of important decisions and choices to create a successful and marketable application. In this paper we investigate and analyze the challenges mobile developers face. A framework for the aggregation of major mobile application distribution channels is introduced with the aim of providing an efficient way to locate, purchase, and download a mobile application onto a mobile device.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".