Book Review | Discard Studies: Wasting, Systems, and Power, by Max Liboiron and Josh Lepawsky (MIT Press, 2022)
Bibliographic record
Abstract
Max Liboiron and Josh Lepawsky start their latest book, Discard Studies: Wasting, Systems, and Power (2022), with an ordinary object that, though seemingly familiar, carries with it a web of invisible and unspoken relations.By exploring the toxic entanglements behind a discarded cash register receipt, Liboiron and Lepawsky take an unexpected path to ask how objects, people, communities, materials, and practices become valued or disposable.Throughout the book, the authors show how wasting is a technique of power and address the role of discarding in the construction and preservation of dominant structures.Discard studies emerged in 2010 as an interdisciplinary field of research initiated by Robin Nagle, founder of the Discard Studies online hub.Scholars in this field look at the why, what, where, and how of waste, and ask questions about the systems that shape and render things disposable.Even though Liboiron and Lepawsky state that they do not seek to provide an overview of the field, those unfamiliar with the discipline might find that the book offers a good introduction to it.In fact, through the introductory chapter the authors discuss some key discard studies concepts and present four methods used by discard studies scholars to debunk common myths about waste-namely, defamiliarization, denaturalization, decentering, and depurifying.Although these methods are often used to think about waste and trash, the authors show, through the theories expounded in subsequent chapters, that it is possible to use these approaches to think more widely about power, inequality, and justice.In chapter two, the authors lay out a theory of scale, discussing the unevenness in relationships within systems and defining scale as "relationships that matter" (45).Using a situated perspective to think about these relationships might help us
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
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".