Lives in Transition: Longitudinal Analysis from Historical Sources ed. by Peter Baskerville and Kris Inwood
Bibliographic record
Abstract
Reviewed by: Lives in Transition: Longitudinal Analysis from Historical Sources ed. by Peter Baskerville and Kris Inwood Lisa Dillon Peter Baskerville and Kris Inwood, eds., Lives in Transition: Longitudinal Analysis from Historical Sources (Montréal and Kingston: McGill-Queen’s University Press 2015) The edited volume Lives in Transition: Longitudinal Analysis from Historical Sources has arrived at a propitious moment when academic researchers are foregrounding the critical importance [End Page 382] of early life conditions on later-life outcomes at the same time that public concern for intergenerational social inequality has intensified. This collection touches on both issues, and more. The chapters in this volume are grouped into four themes – transnational migrations, mobility in the rural world, mobility in the urban world, and ethnicity and war – and encompass 19th and 20th-century Canada, Australia, New Zealand and the United States. While most of the chapters address the lives of free white male groups such as farmers, industrial labourers, and migrating settlers, some chapters address marginalized populations such as convicts and Aboriginals. This book is particularly notable for its integration of methodological innovations with path-breaking evidence on historical life course patterns. This volume serves as an excellent primer on various approaches to constructing linked data sets, usually via census-to-census linkage, but often with the integration of other historic sources. Some of the studies relied upon high-performance computing and machine-learning to develop automatic record linkage programs. Luiza Antonie, Peter Baskerville, Kris Inwood and J. Andrew Ross linked women and men between the 1871 and 1881 censuses to study Canadian occupational mobility while Gordon Darroch linked census microdata from 1861 and 1871 Ontario to study factors conditioning entry into farming. In his analysis of factors predicting movement and persistence in rural Perth County, Ontario, 1871–1881, Baskerville focused on 1871 residents in a smaller geographic unit but then searched for each resident across Canada and the United States in the censuses of 1880/1881; by doing so, Baskerville situated his population at the crossroads of micro- and national history. Kenneth M. Sylvester and Susan Hautaniemi Leonard traced farm operators and their households over time, drawing upon both personal and agricultural schedules of the Kansas census from selected communities. Other scholars broadened their studies by tapping into complementary resources. In his study of US social mobility between 1900 and World War II, Evan Roberts used a survey of Chicago working-class families conducted in 1924 and 1925, linking survey respondents backward to the 1920 census and forward to the 1930 census. John Cranfield and Inwood drew upon the personnel records of the Canadian Expeditionary Force (cef) 1914–1918, linking them to the 1901 Canadian census. Sherry Olson linked Montréal residents enumerated in the 1881 census to the 1901 census, but also attached addresses and rental values from the municipal tax roll to her data, consulted Catholic and Protestant marriage records to help with the matching effort, and used gis to estimate distances between households. By linking aboriginals and mixed-race men in the cef records back to the 1901 Canadian census, Allegra Fryxell, Inwood, and Aaron van Tassel discovered that “Aboriginal participation in the war was considerably more extensive than has been recognized.” (270) The two studies of British convicts transported to Tasmania drew upon British convict records which meticulously recorded extensive details of convicts’ origins, physical characteristics, and experience under sentence, as well as surgeon-superintendent voyage journals. Rebecca Kippen and Janet McCalman used this source to identify a unique set of “character” variables in terms of convicts’ behaviour under sentence; the researchers then searched across a wide variety of genealogical sources for the destiny of each convict. Lenihan also used crowd-sourced genealogical information, a register of 6,243 immigrants of Scottish birth arriving [End Page 383] in New Zealand before 1921. Kandace Bogaert, Jane van Koeverden and D. Ann Herring consult largely qualitative sources, including recruitment materials, church records, advertisements and letters in newspapers and army records, to understand the origins and spread of influenza in 1918 in the Polish Army Camp at Niagara-on-the-Lake. The various papers demonstrate two basic approaches: a national-level or provincial/state-level study which uses...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".