Bibliographic record
Abstract
Neste artigo, invoco o intelectual italiano pós-Segunda Guerra Mundial Pier Paolo Pasolini, justapondo a pedagogia pública de Pasolini – sua presença subjetiva sempre sincronizada com o momento histórico – com um artigo de 2013 composto pelos acadêmicos norte-americanos contemporâneos Jake Burdick e Jennifer Sandlin, que discutem o que chamo de engenharia discursiva, descartando conceitos canônicos de educação (sem argumentos ou evidências), fantasiando que, ao mudar o que dizemos, podemos mudar o mundo. Infelizmente, Pasolini sabia melhor. Sem a segurança de um professor titular, Pasolini arriscou sua vida para ensinar ao público italiano, chamando a atenção para o caminho catastrófico que a humanidade tomou, especificamente substituindo a virtualidade pela realidade, uma tecnologização que imaginamos nos deixar imunes às consequências do capitalismo desenfreado. Com foco no romance inacabado de Pasolini, Petrolio (petróleo ou óleo cru), e em um filme de 2014 que se concentra nos últimos dias antes do assassinato de Pasolini, concluo essa justaposição curricular na esperança de esculpir o que Tetsuo Aoki chamou de espaço generativo de diferença, no qual podemos reexperimentar – até mesmo reativar – um momento antropológico anterior, quando ainda éramos – mais ou menos – “humanos”.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".