Bibliographic record
Abstract
The paper, based on an ongoing research project conducted in Finland, examines voluntary dumpster diving as a practice of valuation. Its main questions are: How is voluntary dumpster diving intertwined with the question of value? And, conversely, what can dumpster diving teach us about practices of valuation more generally? The article proceeds via three steps. First, in order to emphasize the creative side of dumpster diving as a practice of valuation, we draw on Georg Simmel’s theory of value, supplementing it with the concepts of actuality and virtuality, as elaborated by Gilles Deleuze. Second, we look more closely into the practicalities of valuation evident in dumpster diving. It involves a particular orientation to the urban environment that we call the scavenger gaze. Third, the informants also value the practice itself in relation to its societal relevance. They think about dumpster diving as a way of doing good and as part of an ecologically sound form of life. All in all, as value does not reside inherently in waste or would simply be merely the product of subjective judgment, the analyst must attend to multiple modes of valuation evident in the practice, among which there is no self-evident hierarchy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".