A Short Talk on Digital Collaboration Topic Based on Bibliometric Data. 2015-2019
Bibliographic record
Abstract
Requests to Scopus: TITLE-ABS-KEY ( digital AND collaboration ) AND PUBYEAR > 2014 AND ( LIMIT-TO ( DOCTYPE , "ar" ) OR LIMIT-TO ( DOCTYPE , "cp" ) ) — 4,029 document results Publication sources are largely related to — computers, information, communications, intelligent systems, digital libraries and education; from keywords we can build the collaboration chain: humans — management — decision making by digital storage — education and teaching — big data — digital technologies — digital libraries — social networking — artificial intelligence United States — documents/citations/total link strength; 4558/1059 = 4,3; 31835/1059 = 30,06; Russian Federation — 82/60 = 1,36; 1055/80 = 17,58; — low citation and link strength Universities in Canada, the USA and Europe dominate the list. The presence of two Arab universities and the Schlumberger service company is noteworthy. OnePetro Query for collaboration digital, published between 2015 and 2019 has returned 741 results.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.066 | 0.076 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 0.070 |
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".