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Record W2215064346 · doi:10.1515/9780773589391-006

Infrastructures of Census Taking

2014· book-chapter· en· W2215064346 on OpenAlexaboutno aff
Evelyn Ruppert

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2014
Typebook-chapter
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsnot available
Fundersnot available
KeywordsCensusGeographySociologyDemographyPopulation

Abstract

fetched live from OpenAlex

Infrastructures of Census TakingOn 2 June 1911, Thomas Vance, census commissioner for Centre Toronto, expressed delight with the way things had gone so far with the enumeration of his district.Based on his experience overseeing the same district in the 1901 census, things were proceeding ever more efficiently.Vance had accompanied three different enumerators the previous evening during their rounds of "The Ward" to ensure that counting the "foreign population" was going smoothly.And, leading up to census day, he had participated in meetings with the Ontario Special Commissioner Mr J.C. Macpherson and with the other four commissioners responsible for Toronto.He had also overseen the hiring of enumerators and their training based on instructions issued by Archibald Blue, the chief census commissioner for the Dominion of Canada.Vance was part of a vast operation that included many actors beyond census officials.There were, of course, the politicians.At the top of the list was Minister of Agriculture Sydney Fisher, who was responsible for the "counting of noses."As part of Sir Wilfred Laurier's Liberal government, which had been in office since 1896, Fisher was well aware of the implications for parliamentary representation of this decennial enumeration.Since the last enumeration in 1901, two new provinces, Saskatchewan and Alberta, had become part of the Canadian federation, adding ever more numbers to an expanding western population.It was generally agreed that the count would result in the redistribution of seats in the House of Commons with the west making significant gains.In addition to the politicians, Vance was painfully aware of, yet also dependent on the interventions of civic officials, boards of trade

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.433

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0050.005
Scholarly communication0.0110.007
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1290.060

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 head0.036
GPT teacher head0.255
Teacher spread0.219 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueMcGill-Queen's University Press eBooksSame topicCensus and Population EstimationFrench-language works237,207