Bibliographic record
Abstract
With the publication in portuguese of Commonwealth, written by Michael Hardt and Antonio Negri (2016), The common: essay on revolution in the 21st Century, by Christian Laval and Pierre Dardot, and Caliban and the witch, de Silvia Federici, the theme of the commons returned to gain attention of researchers of the human and social sciences in Brazil. In this article, we recover an effort made by Sergio Amadeu da Silveira in 2007 when he published the article “The concept of commons in cyberculture”, and introduce different authors who are working with the concept of the commons, contributing to describe and locate part of the bibliography available on the subject, much of which is still unavailable in Portuguese. In the article, we deal with the work of authors such as Garret Hardin, Elinor Ostrom, David Bollier, Laval and Dardot, Hardt and Negri, Silvia Federici, Michel Bauwens, Silke Helfrich, Imre Simon, Miguel Said Vieira, Joan Subirats and César Rendueles, Yochai Benkler, Rafael Zanatta and Ugo Mattei, among others.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 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".