Collective Intelligence, the Future of Internet and the IEML: Interview to Pierre Lévy by Art Farley and Massimo Lollini
Bibliographic record
Abstract
Collective Intelligence, the book in French, that Pierre Levy wrote before the existence of the worldwide web, when only the Internet existed, it's a philosophical vision of the future, a philosophical vision of what could be a global civilization based on the digital and the general interconnection of all the computers. In the interview, Levy addresses the creation o the WWW by Tim Berners Lee as a form of collective intelligence. He then discusses Berners Lee's proposal for a reform of the WWW to avoid its confiscation by the big platforms, such as Google, Facebook, and Amazon. Then, he introduces his most recent project the IELM as a tool for semantic metadata and a code between the natural languages and the algorithms, and all the apparatus of computations.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".