MétaCan
Menu
Back to cohort
Record W3109164637 · doi:10.1215/00182702-8816613

Constructing Cost of Living Indexes

2020· article· en· W3109164637 on OpenAlexaboutno aff
Cecilia T. Lanata-Briones

Bibliographic record

VenueHistory of Political Economy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEliteIndex (typography)LegitimacyPoliticsCost of livingQuarter (Canadian coin)Intervention (counseling)EconomicsArtifact (error)SociologyPolitical economyPolitical scienceEconomic growthLawGeographyPsychology

Abstract

fetched live from OpenAlex

This article examines the first two estimates of the Argentine cost of living index, focusing on their producers, Alejandro Bunge and José Figuerola. The Bunge index, released in 1918, did not hold as a stable social and political artifact because it lacked legitimacy in the eyes of many sectors of society. This was a consequence of Bunge’s personal connections, and of the close relationship between the index and Bunge and between the index and his macroeconomic vision, which differed from that of the economic and political elite. The trajectory of the second estimate, released in 1935 by the National Labor Department, highlights the importance of the working class as a social actor in fostering the adoption of the cost of living index. The legitimacy of the National Labor Department’s index was enhanced by the connections between Figuerola and the International Labour Organization. The contrast between the two histories suggests that for a cost of living index to hold as a stable social and political artifact during the first half of the twentieth century, a connection between the index and industrial relations had to exist. In particular, the index should contribute toward the formation of the working class as a visible object for policy intervention.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.218
Teacher spread0.169 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueHistory of Political EconomySame topicEconomic Theory and PolicyFrench-language works237,207