Bibliographic record
Abstract
,In generic terms, API is a way for two applications to communicate with each other over a network using a common language. It has evolved to be a powerful tool for companies across various industries such as banking, healthcare, online retail, and others, to speed up their business operations. APIs are an integral part of the digital economy. Due to the non-availability of API economy data, this research shows the contribution of a selected sample of API companies in strengthening the digital economy. In Objective 1, this research has measured the growth of the APIs economy and digital economy at the macro level, Objective 2 measures the growth pattern of each company in the sample, Objective 3 identifies the APIs-related constraints through a literature review, Objective 4 classifies APIs related constraints into three different categories i.e. APIs as a Product constraint, APIs as a Service constraint and APIs as a Product-Service constraint. A review of the literature on this subject has shown that there are constraints related to Scalability, Manageability, Security, and possibly other challenges that restrict the building of an effective ecosystem of APIs. Therefore, an exploratory study-based approach has been taken in this research that helps in measuring the growth of companies in the presence of API-specific constraints/challenges that create roadblocks in achieving companies’ objectives. Overall, the findings of this research will help in creating new knowledge and information about various APIs specific constraints, risks, and challenges that affect APIs and Digital Economy’s growth.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".