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Record W800497077 · doi:10.1162/jinh_r_00819

<i>The Dawn of Canada’s Century: Hidden Histories</i>. Edited by Gordon Darroch (Montreal, McGill-Queen’s University Press, 2014) 498 pp. $100.00

2015· article· en· W800497077 on OpenAlexaffabout
Barry Edmonston

Bibliographic record

VenueThe Journal of Interdisciplinary History · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMicrodata (statistics)CensusAmerican Community SurveyGeographyPopulationGenealogyPoliticsRegional scienceDemographyHistorySociologyPolitical scienceLaw

Abstract

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This book, written by many of Canada’s leading historical scholars, sheds new light on a number of topics, including language identity, aboriginal populations, family and household arrangements, immigration, politics, and social class and mobility at the beginning of the twentieth century. Its most important feature is the analysis of historical census microdata. The four introductory chapters deal with census data and methods, the new 1911 census microdata sample, and parallel projects for samples of the 1851/52, 1871, 1881, 1891, and 1901 censuses. In addition to the 1911 census sample, comparable samples are also underway for the 1921, 1931, 1941, and 1951 censuses. The other twelve chapters present an analysis of historical census microdata. All of them analyze 1911 data, but several use other historical data sets. A chapter about Quebec City explores the linkage of individuals over several censuses for the 1871 to 1911 period. About one-half of the chapters examine Canada’s national population. Other chapters deal with subnational populations, including Trois-Rivières, Newfoundland, Quebec City, and Hamilton.The development and availability of census microdata samples has been the basis for improvements in research during the past five decades. The methodological foundation for these new data emerged in the 1930s and 1940s when statisticians provided the theory and methods for survey sampling. By the 1950s, researchers were able to conduct national surveys of 1,000 respondents, with valid inferences about entire populations. Because of new sampling methods, better statistical software, and big advances in computing technology, census microdata has progressed in four stages.First, survey sampling techniques were employed for the first time after the 1960 U.S. census to make data tapes with public-use microdata samples (pums) available. These pums files were either 1-in-1,000 or 1-in-100 (1 percent) samples of individual records—omitting identifiers that could reveal individual identities—with information about age, sex, ethnic origin, nativity, marital status, family relationships, occupation, income, and other data collected in the census. These first pums files proved to be a treasure chest; researchers could, for the first time, prepare their own tabulations rather than depend on tables published by the U.S. Census Bureau. Moreover, researchers could use modern multivariate statistical techniques for the analysis of census microdata. By the end of the 1960s, academic journals routinely included articles with regression and other multivariate analysis of census data.The second major advance occurred when census microdata for several countries became available for comparative analysis. By the 1970s, the census microdata samples for many countries could be used for the study of such topics as the factors associated with international variations of female employment.When researchers realized the value of pums files for several censuses, they began to develop pums files for earlier censuses—the third stage. In the United States, researchers initially took samples from the 1940 and 1950 censuses in order to make longer-term comparisons with existing 1960, 1970, and 1980 censuses. Currently, 1 percent pums files exist for U.S. decennial censuses from 1850 to the present, comprising one of the most valuable quantitative data sets for historical research.We are currently in the midst of a fourth stage—the development of large census-data collections that are both historical and comparative, as evidenced by the pioneering work of ipums-International (https://international.ipums.org/international/), which now includes pums files for 258 censuses from 79 countries. For a comparative study of, say, southern Latin America, ipums-International currently includes sixteenth census pums files for Argentina, Chile, and Uruguay that could be used to analyze trends from the 1960s to the present. The next frontier for census microdata analysis is the comparative study of change over time in, for instance, the determinants of fertility variations, correlates of family structure, and factors affecting the living arrangements of elderly adults.U.S. census microdata files—including individual data with detailed codes for age, country of birth, ethnic origin, and place of residence (though no personal identifiers)—are usually available for public use, as evidenced by their availability for download from ipums-usa (https://usa.ipums.org/usa/). Canadian census data, however, are relatively restricted. Some of it, such as census microdata samples for the 1921 to 1951 censuses, are available only within special limited-access research data centers. Moreover, Canada’s pums are more limited than comparable data in the U.S. and some other countries. For example, information about place of residence is limited to the several-dozen-largest metropolitan areas (compared to several hundred cities of smaller size in the U.S. pums files). Moreover, Canadian public-use data on couples is confined to the ethnic origin of only three categories for husbands and wives—British, French, and other—thus preventing analysis of ethnic intermarriage.pums have several distinctive advantages over files in data centers or other facilities that limit the access and release of data tabulations. Although researchers can prepare tabulations and multivariate analysis with both public-use and restricted data, public-use data offer significant advantages in three situations: (1) Analysis of individual census data often requires supplementing the pums files with other data, including contextual variables like the unemployment rate in a city or town. Public-use data facilitates downloading such information from internet sources and linking contextual variables to individual records. Since restricted-data centers often prohibit internet connections and prohibit researchers from entering with other electronic data, they inhibit the development of new data sets. (2) New data files can be created within a restricted data center, but, at least in Canada and probably other countries, they cannot be removed from the center. Hence, researchers who spend considerable time linking individuals with their spouses, children with their mothers, or adults with information about their household cannot easily share their findings with researchers outside the data center. This problem relates to another particularly important issue—(3) the difficulty of replicating empirical research conducted within a restricted data center because other researchers may not have access to the original or intermediate files.The Dawn of Canada’s Century provides a valuable source of historical evidence about the development of Canada at the beginning of the 1900s. It should appeal to readers and scholars in search of a systematic and stimulating treatment of historical census data.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.146
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0060.003
Scholarly communication0.0080.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0580.015

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreReview

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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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