Bibliographic record
Abstract
Mashups combine data and services provided by third parties through open APIs (such as Google Maps and Flickr), as well as internal data sources owned by users. The creation of mashups is supported by a complex ecosystem of interconnected data providers, mashup platforms, and users. In this paper, we examine the structure of the mashup ecosystem and its growth over time. Several observations follow from our analysis. First, we can conclude that while the number of new APIs and mashups over time follows a linear growth pattern, the distribution of mashups over APIs is not uniform but follows a power law. This implies that a small number of APIs provides the basis for the majority of mashups, and the other APIs are only used in certain application niches. Second, our analysis suggests that mashup platforms were introduced in response to the increasing complexity of mashups, as mashups evolved from one-feature mashups (widgets). Third, we observe that complementary relationships between open APIs are formed based on the position of the APIs in the ecosystem. The propensity of two APIs to be used together in the same mashup depends on the existing number of mashups to which they both contribute. The growth of the mashup ecosystem follows a pattern where keystone data providers or ‘powerful hubs’ attract niche data providers as complementors, and the positions of keystones in the ecosystem are mutually reinforcing.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".