MétaCan
Menu
Back to cohort
Record W2783800071 · doi:10.1111/ajes.12215

Real Estate, Public Works, and Political Organization in Winnipeg, 1870–1885

2018· article· en· W2783800071 on OpenAlexaboutno aff
Gustavo Velasco

Bibliographic record

VenueAmerican Journal of Economics and Sociology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsEliteSettlement (finance)State (computer science)Economic powerEstateReal estatePower (physics)SpeculationEconomyPolitical scienceEconomicsLawFinance

Abstract

fetched live from OpenAlex

Abstract The second half of the 19 th century represented an era of great territorial expansion in almost all the countries of “recent settlement.” In Canada, Winnipeg, the capital of the Province of Manitoba, went from a small hamlet located at the confluence of the Assiniboine and Red Rivers to become the third largest Canadian city at the turn of the century. I argue that the development of a real estate market and the organization of the local political institutions in Winnipeg were interconnected mechanisms that the emerging business elite used to obtain political and economic power during the years of city organization (1870–1885). The disputes over land ownership and the uncertain distribution of land titles among parties related by business and family ties showed how individuals exploited the weakness of the state to secure personal benefits. In this era, old settlers, newcomers, speculators, and business representatives of central Canada and British firms, acting alone or in partnership, attempted to obtain political control of a city in its making and to acquire power and economic benefits through the commodification of urban land. After a period of corruption and mismanagement, a new group organized within the Board of Trade obtained political control of the city and initiated a new cycle of political stability.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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 designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Economics and SociologySame topicCanadian Identity and HistoryFrench-language works237,207