Researchgate.net crawler and a new contribution determines sequence (CDS) method
Bibliographic record
Abstract
General and Focus crawlers are the main types of web crawlers used for different goals, with different crawling techniques and architecture. Our crawler was written in Java language using different software and libraries. To test the crawler, it has been run on the academic social network, Researchgate.net from 3 rd.April to 28th.June 2014 and retrieved real data. The crawler consists of three main algorithms to crawl information such as researchers details, publications details, questions/answers activity details. The retrieved data has been analyzed to highlight the performance of Canadian researchers, in the field of Computer Science on Researchgate.net. Data analysis has been done from the collaboration and (alt)metrics perspectives. Among other features Researchgate.net came with “Impact Points” and “RG Score” (alt)metrics. The former builds on ISI Journal Impact Factor, which disregards author’s contribution in its calculations. A new Contribution Determines Sequence (CDS) method has been developed and tested, with all required scripts which showed better performance than other methods.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".