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Record W2055796288 · doi:10.1007/978-1-137-07182-8_5

Rationalizing Consumption: Lejaren à Hiller and the Origins of American Advertising Photography, 1913–1924

2006· book-chapter· en· W2055796288 on OpenAlexaff
Elspeth H. Brown

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2006
Typebook-chapter
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConsumption (sociology)Middle classRationalization (economics)AdvertisingSituational ethicsMarketingPolitical scienceSociologyBusinessSocial scienceLaw

Abstract

fetched live from OpenAlex

B y the second decade of the twentieth century, the rationalization of the American economy threatened to founder, not on the shoals of production or distribution, where mechanization and national transportation systems had nearly vanquished challenges to middle-class material abundance, but on those of consumption. As numerous historians have argued, advertising matured as a profession in response to a new problem for American business: how to stimulate demand among white, middle-class consumers for the machined cornucopia of standardized products filling the shelves of American retail establishments. Whereas earlier advocates of American productive efficiency, such as the motion-study experts Frank and Lillian Gilbreth, had championed the use of photography in rationalizing the working body in production, by the 1920s the influence of applied psychology had reoriented managers toward an appreciation of the mind as the critical element of rationalized consumption. 2 Achieving greater sales in an increasingly competitive and national marketplace required convincing hesitant consumers that individual difference and personal meaning could be theirs, despite a regularized landscape of standardized goods. Corporations increasingly hired advertising agencies and their creative staffs, in Jackson Lears’ phrase, to “surround mass-produced goods with an aura of uniqueness” designed to stimulate consumption through the promise of individuality. 3 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.018
Scholarly communication0.0060.006
Open science0.0010.002
Research integrity0.0020.003
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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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