Understanding the structure, characteristics, and future of collective intelligence using local and global bibliometric analyses
Bibliographic record
Abstract
"Collective Intelligence" has been a popular area of research for more than a decade. We apply two different analytical approaches (local and global bibliometric analysis) to describe how this literature is organized and how it has evolved. A local approach focuses on the 3,138 articles indexed in the Scopus database where 'collective intelligence' is in the title, abstract, or keyword. A global approach reclassifies all of the Scopus documents into research communities using all (1.28 billion) citations in the database and proceeds to identify which research communities are populated by the 3,138 Collective Intelligence (CI) articles. These two approaches provide significantly different perspectives on how CI is structured, who the leaders of the field are, and how it is evolving. A synthesis of these two perspectives provides ideas for those who wish to contribute to the collective intelligence field. Our findings support the Kuhnian idea of research communities as a useful concept in bibliometric analysis.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".