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Record W7018066907

Collaborative repair as dealienation: an exploration of degrowth technology practice

2023· dissertation· en· W7018066907 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsDegrowthScholarshipNormativeImpossibilityTechnological changeArgument (complex analysis)PoliticsWork (physics)CommodityDisciplineCounterfactual thinking
DOInot available

Abstract

fetched live from OpenAlex

The degrowth hypothesis could be summarised by the following: even if limitless growth were biophysically possible—which it is almost certainly not—it would not be desirable. The degrowth project bills itself as more than just critique: it is “a normative concept with analytical and practical applications” (Kallis et al 2018). Yet while scholarship has meaningfully engaged with ecological economics and political ecology to interrogate the metabolic imbalances and distributional asymmetries of growth-centric society, empirical investigation of alternative “living degrowth” are rare (Brossman and Islar 2020). Degrowth research focusing on questions of technological normativity, or “technology practice” (Drengson 1995) are few and mostly limited to work adopting largely quantitative, or metabolic, approaches to technology, for instance in the ‘low-tech’ movement. These predominantly biophysical framings are clearly necessary in apprehending, and acting upon, the impossibility of endless growth and commodity innovation/production. They are however, less adequate in accounting for the undesirability of endless growth and material accelerations, and in indicating new, more desirable pathways for technology practice moving forward. \nThe present empirical study consists in first-person observation and interviews carried out in a Montreal amateur repair community in 2021-2022. The phenomenon of collaborative repair, or Repair Cafés, is a practice geared to the downscaling of material throughput through the collectivisation of tools, space and repair knowledge. Through observation and analysis, a cluster of questions was asked: how could we begin to think about degrowth technology practice? What would it look like? Can the features of collaborative repair offer us hints? Drawing on recent scholarly efforts to revive ‘alienation’ as a valid theme for social inquiry, and in addressing the noted need for degrowth to think more seriously about “dealienation” (Brownhill et al 2012), the present study looks at collaborative repair as a testing site for the suitability of these concepts, and for their potential application in a proposed degrowth research mandate focused on technology practice. This study is founded on a methodological conviction that when one engages in practice, one not only does something, one also understands that one is doing something, inevitably investing the action with meaning (Jaeggi 2018). From this point of view, and beyond metabolic and redistributive ends, collaborative repair effects a rehabilitation of meaningful subject-subject, subject-time and subject-object relations—relations typically characterised by alienation in industrial commodity economies. The present study also recommends that degrowth think seriously about “resonance” (Rosa 2019) as a more useful and coherent alternative to ‘autonomy’ when conceptualising alienation’s ‘other’. Such a framing appears critical for both elaborating a degrowth critique of technology and enriching discussions of how degrowth normativity bears on practice. �

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0110.050
Scholarly communication0.0130.020
Open science0.0040.013
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.049
GPT teacher head0.381
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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