Bibliographic record
Abstract
O presente ensaio visual foi desenvolvido por Michel Peterson e Christian Peterson para a Revista Paralelo 31. As imagens e textos fizeram parte do conteúdo da Exposição Internacional no espaço expositivo A Sala, no Centro de Artes da UFPEL em maio de 2017. O ensaio propõe uma imersão numa pequena parte de conteúdo textual e imagético vasto, que já foi levantado pelo projeto de pesquisa do ROBAA (Roads of Bones And Ashes / A estrada dos ossos e das cinzas). Esta composição apresenta-se como um convite à reflexão, de uma perspectiva geopoética que, com grupo multidisciplinar, propõe problematizar os genocídios contemporâneos e seus apagamentos.Helene Sacco Where are/lie the bones...This visual essay was developed by Michel Peterson and Christian Peterson for the periodical Paralelo 31. The images and texts are from an international exhibition which took place at the gallery space A Sala, located at the Centro de Artes of the Federal University of Pelotas-UFPel, Brazil, in May of 2017. The visual essay proposes to immerse us in a small part of the vast pool of texts and images, which have been gathered through the research project ROBAA (Roads of Bones And Ashes ). This composition, presented as an invitation towards reflection that, with a multidisciplinary group, proposes to put a geopolitical perspective on contemporary genocydes and their obliteration.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".