Public and Ordinary Bad Feelings: Neoliberal Depression and its Art
Bibliographic record
Abstract
<p>This paper attends to contemporary literature that looks at depression within wider political, social, and economic contexts. The core scholars examined in this section include Byung-Chul Han, Bernard Stiegler, Naomi Klein, and Michel Foucault. Through a review and critical analysis of their texts, this section of the paper demonstrates that depression should be thought of as a reasonable affective response to neoliberal capitalism. The second half of this paper examines various theories about the relationship between creative practice, art, technology, and neoliberal depression. Authors on this topic include Christine Ross, Ann Cvetkovich, Nicolas Bourriaud, Anthony Dunne, and Fiona Raby. By attending to the intersection of creative practice and neoliberal depression, this paper acts as a survey of the field, thereby uncovering what art can contribute to this vital discourse.</p>
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".