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Record W2317188857 · doi:10.1177/0896920513504603

Interrogating the Algorithm: Debt, Derivatives and the Social Reconstruction of Stock Market Trading

2014· article· en· W2317188857 on OpenAlexaff
Laureen Snider

Bibliographic record

VenueCritical Sociology · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsQueen's University
Fundersnot available
KeywordsDebtPoliticsEconomicsStock marketAlgorithmic tradingHarmDerivatives marketHigh-frequency tradingInstitutionStock (firearms)Alternative trading systemMarket economyFinanceLawPolitical science

Abstract

fetched live from OpenAlex

This article examines the rise of algorithmic trading and the invention of new financial instruments, their implications and effects. It politicizes and denaturalizes the technological ‘innovations’ that have created networks of control over groups that states and corporations have designated as threatening, then examines how such ‘innovations’ have transformed stock market trading from a geographically-fixed, paper-based institution to a high-speed electronic casino. Finally, it discusses how and why financial actors have amassed the social, economic and political power that allows them to engage in practices that cause immense social harm while remaining virtually untouched by state regulatory agencies, civil and criminal law.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.268
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations37
Published2014
Admission routes1
Has abstractyes

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