PARALEL EVREN SAVAŞ BARIŞ DE UĞUR GALLENKUŞ: UMA IMAGEM VALE MAIS QUE MIL PALAVRAS? A ARGUMENTAÇÃO ATRAVÉS DA IMAGEM
Bibliographic record
Abstract
Reading images is part of our daily lives. However, are we able to identify the persuasive force that many images carry? The present work aims to draw a descriptive analysis of the material components that build one of the images of the series “Paralel Evren Savaş Bariş” by Uğur Gallenkuş. Furthermore, we will present the possible meaning effects raised through the argumentation. The work is justified as it seeks to develop a systematic learning about reading images. We take as theoretical foundation the postulates of Patrick Chauraudeau (2013) and Barthes (1990) regarding the process of image construction and Mendes’ image grid (2010) for the levels of construction of meaning, of images. Finally, we will use the contributions of Koch; Elias (2018) and Charaudeau (2016) regarding to argumentation. As a result, it is expected to contribute to a significant teaching of reading, whether verbal and/or non-verbal texts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".