Let’s re-search. APIs and web-scraping: A technical note on the ResearchGate case
Bibliographic record
Abstract
This paper explores the growing need for more efficient and streamlined methods of retrieving academic data, especially when working with extensive datasets.It provides a detailed comparison of three retrieval techniques: manual searches, web scraping, and Application Programming Interfaces (APIs).While manual methods remain effective for small-scale searches, they quickly become impractical for large data volumes.In contrast, web scraping and APIs offer automation that significantly accelerates data collection.However, platforms like ResearchGate currently limit the use of these automated methods, forcing researchers to rely on manual processes.This paper advocates for ResearchGate to implement APIs, akin to those of Scopus and Web of Science, to provide controlled, secure, and efficient access to academic literature.Such advancements would be particularly beneficial to researchers from developing nations and institutions without access to subscription-based services.Furthermore, enabling automation would democratize access to scientific resources and foster a more inclusive global academic environment.
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 | 0.049 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.034 | 0.079 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".