Bibliographic record
Abstract
In this dissertation I explore the relationship between politics, aesthetics, culture and technology by (re)thinking and (re)conceptualizing the concept of kitsch as a theoretical construct in order to investigate the dream-worlds of Europe which sprang at the intersection of liberalism, social democracy and capitalism. I argue that the unexplored potentialities of kitsch, as a concept, reside in the analysis of the dream-worlds, which have been occupying the social and political imaginaries of Western individuals, communities and institutions since the disenchantment of the world. My methodological approach is built on Benjamin's notion of historical materialism. Thus, I engage with the historical object(s) (e.g., arcades, fashion, technological reproductions etc.) not as "object(s) of experience" but as a "participant(s) in historical experience" (Caygill 2004, 90). Challenging the progressive notion of history, I argue that within the objective impenetrability of commodity fetishism a "sur-real" world of fetishized images -that is, kitsch -emerges, alienated from the individual and the collective, yet constituting and shaping them. By mapping out the implications of this "sur-real" world on "the political," the collective (un)conscious and action, I conclude that alternative politics could arise from the unsettling interpretations of the reified and symbolic expressions of this same "sur-real" world, paving a path for new political imaginaries.
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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 | 0.003 |
| 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 | 0.027 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".