Bibliographic record
Abstract
At a time when ever-rising smartphone sales are driven as much by demand for devices that run must-have third-party apps as by the quality of traditional voice and data services, there is a myriad of challenges facing the software developer who is looking to choose which mobile development software platform to invest in. Written from the perspective of an established consumer bank that is about to commence development on its first downloadable application for mobile devices, the case surveys the state of the smartphone market in 2010 and considers the challenges of a platform landscape that includes significantly varying installed device base sizes, growth rates, application distribution models, and hardware device profiles. Focusing on Apple's market-leading iOS platform and App Store, for iPhones and other devices and on Google's developing Android OS and associated Android Market, the case considers potential benefits and pitfalls of each and touches on the reasons that other longer-standing platforms, such as RIM's BlackBerry platform, are less appealing to modern-day application developers.Learning Objective:In this case, an application developer needs to decide for which of the two competing platforms (iPhone or Android) she wants to develop her application first. It allows a rich discussion on the incentives that attract application developers, as well as on the motivation for different style of platform governance. The case also illustrates the difficulty of managing applications on a platform.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".