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Record W4411166972 · doi:10.51270/47.2.209

Bringing in the Children: The Impact of Social Archaeology on Archaeological Studies of Childhood in Southern Ontario

2023· article· en· W4411166972 on OpenAlexvenueaboutno aff
Steven Dorland

Bibliographic record

VenueCanadian Journal of Archaeology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArchaeologyHistoryAnthropologySociology

Abstract

fetched live from OpenAlex

In this paper, I present a historical overview to evaluate methodological and theoretical contexts that have impacted the study of Indigenous childhood in archaeology in the Great Lakes region. Until recently, the study of childhood practice has been largely overlooked, and our understanding of childhood has been limited to bioarchaeological studies of ancestral remains to address questions of health, diet, and disease, with less focus on childhood practices. Rather than a paucity of empirical data, I suggest that it is a theoretical emphasis on cultural history and its legacy that has resulted in restrictive models. Recently, emerging scholars have contributed significantly to the development of methodological and theoretical frameworks and have begun asking broader questions about identity and knowledge production. In this paper, I highlight the impact social archaeology has had on the archaeological research of Indigenous childhood in the Great Lakes region. I follow by identifying trends and future directions of childhood studies that are currently being pursued in the Great Lakes region. Growing our understanding of childhood in the past not only fleshes out past actors in archaeological narratives but also enhances understanding of broader social and economic practices in the region and provides frameworks to contribute to the broader theoretical arguments taking place in the anthropology of childhood.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.009
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.311
Teacher spread0.269 · 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
Published2023
Admission routes2
Has abstractyes

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