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Record W4293243843 · doi:10.23889/ijpds.v7i3.2076

Linking Eight Decades of Canadian Census Collections.

2022· article· en· W4293243843 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal for Population Data Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCensusData scienceComputer scienceLeverage (statistics)PopulationRecord linkageDemographicsData qualityLinkage (software)GeographyData miningDemographyMachine learningSociologyBusinessMarketing

Abstract

fetched live from OpenAlex

IntroductionLinking the many decades of census data collected during Canada’s settlement allows researchers to investigate the movement patterns of early settlers, changes in regional demographics, and intergenerational mobility. This research leverages new methodologies and previously untranscribed individual attributes to link for the first time Canadian censuses from 1852 to 1921. Objectives and ApproachThis work aims to build upon prior efforts (1871-1901 linking) to link the decennial Canadian censuses spanning from 1852 to 1921. We use a more complete transcription of the censuses, that has recently become available to researchers through The Canadian People’s project. Our approach to this task begins with reproducing the results of previous work using data from this new transcription. From there, we add additional time-invariant individual characteristics as features to our classification model. We also explore newer methodologies designed to leverage household information during the linkage process, with the goal of increasing the linkage rate. ResultsWe describe the differences between the different methodologies we use, and the steps we took to clean and standardize the data. We compare the links produced by the different methodologies in terms of the number of links yielded, their quality (false positive rate), and certain aspects of the bias present in the resulting collections of links. We discuss the challenges and potential approaches to dealing with sections of the population who remain difficult to link. We expect the advancements in record linkage methodologies for historical populations coupled with this more complete transcription of the censuses to offer advantages over prior approaches when linking these censuses. We expect the resulting linked data to offer new insight into Canada during this time period. Conclusions/ImplicationsThe resulting collection of linked data over this time period should characterize approximately three generations of early Canadians. This linked data will be passed on to other researchers and will allow us to better understand the changing experiences of the Canadian population during these early stages of Canada’s development.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.096
GPT teacher head0.408
Teacher spread0.312 · 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