From address to outcome, a proposal for discussing research in the art academia towards the idea of a critical landscape
Bibliographic record
Abstract
This text aims to discuss forms of teaching/learning that allow for the understanding of the involvement of students in carrying out actions that pertain to two major areas of intervention: landscape and knowledge, and how research processes may be generated by those actions. Landscape is intended to be approached from a dynamic and critical point of view, beyond its multiple senses and descriptive characters, such e.g. as rural or urban considered as limiteded descriptions. Knowledge is considered horizontally as a collectively generated process focused on providing tools for research and analysis based on student-centred actions. As a brief open-ended exercise, this text does not aim to respond to a set of challenges involved in the definition of the practices that will attempt to discuss, such as, firstly, the contradictions inherent in the definitions of trans or post-medial practices, in constant change and often contested from current theory and art itself; and second, the danger of enclosing ourselves in definitive terminologies to describe the practices that occupy us and that often operate precisely in opposition to the propensity to find and stabilise definitions, which is the aspiration of the academia. How is academic research in the art academia to deal with these contradictions and how to distinguish between practice based and practice led research, will be the key questions that the text will try to address critically. Is the space of the academia the last space for utopia? Cover page: Relational
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".