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Record W2742372087 · doi:10.1111/johs.12169

Banking on Identity: Constructing a Canadian Banking Identity One Branch at a Time

2017· article· en· W2742372087 on OpenAlexaffabout
Simarjit S. Bal

Bibliographic record

VenueJournal of Historical Sociology · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsScope (computer science)NationalismOrganizational structureIdentity (music)Space (punctuation)Branching (polymer chemistry)Control (management)BusinessEconomicsMarketingPolitical scienceComputer scienceManagementPoliticsLaw

Abstract

fetched live from OpenAlex

Abstract This paper seeks to explore the role that the Canadian branch banking structure has played in producing a national Canadian economic space as well as nationally oriented conservative Canadian banking subjects. Explosive growth in the scope of Canadian bank branch networks between 1880 and 1930, both in terms of number of branches and their geographic range, forced banks to re‐evaluate their management practices. To manage an increasingly unwieldy structure, banks worked to centralize control and homogenize operations and the bankers themselves. Through centralization, bank head offices developed more robust branch reporting tools, which allowed them collect and repurpose disparate data into new national level information and knowledge. Working as centres of calculation, bank head offices used this new information to integrate a nationalist outlook throughout the network, deploying disciplinary technologies and techniques, in an effort to detach bankers from a local or regional orientation. This paper shows that, rather than merely a tool for efficient allocation of capital, the branching structure is a productive socio‐technical structure, which helped to construct the very nature of the national space it sought to manage.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.407
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.056
GPT teacher head0.252
Teacher spread0.196 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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