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Record W2265080764 · doi:10.3167/latiss.2015.080202

'Our Table Factory, Inc.': Learning Marx through role play

2015· article· en· W2265080764 on OpenAlexaff
Neda Maghbouleh, Clayton Childress, Carlos Alamo-Pastrana

Bibliographic record

VenueLearning and Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsCapitalismProletariatSociologySurplus valueAlienationCommodificationBourgeoisieCommunismMarxist philosophyCapital (architecture)Social capitalSocial scienceNeoclassical economicsEconomicsEconomyLawPoliticsPolitical science

Abstract

fetched live from OpenAlex

Marx's critique of capitalism remains foundational to the university social science curriculum yet little is known about how instructors teach Marx. In post-industrial, service-oriented economies, students are also increasingly disconnected from the conditions of industrial capitalism that animate Marx's analysis. Inspired by the discussion of how a piece of wood becomes a table in Marx's Capital Vol. 1., 'Our Table Factory, Inc.' simulates a diverse array of roles in the chain of production into and out of a table factory to understand key concepts: means/mode of production, use/exchange value, primitive accumulation wage/surplus labour, proletariat, bourgeoisie, alienation, false consciousness, commodity fetishism and communist revolution. We describe the exercise and present qualitative and quantitative assessment data from introductory sociology undergraduates across three small teaching-intensive universities in the United States. Findings detail the exercise's efficacy in fostering retention of material and in facilitating critical engagement with issues of inequality.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.020
Scholarly communication0.0060.010
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.134
GPT teacher head0.424
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2015
Admission routes1
Has abstractyes

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