O Manifesto do Trabalho: Democratizar, Desmercantilizar e Remediar
Bibliographic record
Abstract
Portuguese and Brazilian book launch: "Manifesto do Trabalho: Democratizar, Desmercantilizar e Remediar," coordinated by Flávia Máximo (Federal University of Ouro Preto - UFOP), Ana Virginia Moreira Gomes and Eduardo Rocha Dias (University of Fortaleza - UNIFOR). The Brazilian version of the book, "Manifesto do Trabalho," comprises the twelve chapters, translated into Portuguese, from the original French version "Le Manifeste Travail." The latter has been written by Isabelle Ferreras (FNRS-Louvain, Belgium & Harvard LWP), Julie Battilana (HKS/HBS, Harvard, USA), Dominique Méda (Paris-Dauphine PSL, France), Adelle Blackett (McGill, Canada), Julia Cagé (Sciences Po-Paris, France), Neera Chandhoke (University of Delhi, India), Lisa Herzog (University of Groningen, the Netherlands), Sara Lafuente Hernandez (ETUI-ULB, Belgium), Hélène Landemore (Yale, USA), Flavia Maximo (Universidade Federal de Ouro Preto, Brazil), Imge Kaya Sabanci (IE Business School, Spain) and Pavlina Tcherneva (Bard College-Levy Institute, USA). Moreover, the Brazilian version also features six new chapters, written by Brazilian researchers, who form a group characterized by diversity of race, gender identity, and sexuality: Maíra Neiva Gomes (State University of Minas Gerais), Fernanda Barreto Lira (Federal University of Pernambuco), Valdete Souto Severo (Federal University of Rio Grande do Sul), Regina Stela Corrêa Vieira (University of West Santa Catarina), Jaqueline Gomes de Jesus (Federal Rural University of Rio de Janeiro), Louisa Acciari (University College London) and Renata Queiroz Dutra (National University of Brasilia). From a decolonial perspective, the Brazilian book situates the #democratizingwork principles into the Global South's labor relations.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".