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Record W7006170088

Structural violence in Éire: The bone histology of victims from the Great Famine (Kilkenny, Ireland 1845-1852)

2021· dissertation· en· W7006170088 on OpenAlexaboutno aff

Bibliographic record

VenueOtago University Research Archive (University of Otago) · 2021
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFaminePopulationSubsistence agricultureDiseaseQuarter (Canadian coin)BioarchaeologyCause of deathJoint disease
DOInot available

Abstract

fetched live from OpenAlex

This project uses bone histological analysis to explore how disease, diet, and social circumstances affected the bone remodeling of a group of people who died during the Great Famine in Ireland between 1845 and 1852. The human remains discovered on the grounds of the nineteenth century Kilkenny Union Workhouse in Kilkenny City, Ireland represent a well contextualized, homogenous group of people who died due to stress induced by the Great Famine sometime between 1847-1851. These factors make this an ideal population to study the biological effects of structural violence and for investigating the meaning of skeletal lesions, often referred to as the “osteological paradox”.
\nIn 1845, a potato blight wiped out the primary means of subsistence for the poor in Ireland, causing the country to lose a quarter of its population to death and migration. This event had a massive cultural, demographic, and biological impact on the world and has been the source of international scholarly interest in the recent past. Historical research has suggested the poor in Ireland suffered and died from comorbidities of infectious and metabolic disease due to food insecurity, the cause of which is debated but is often attributed to conflicts with England. Bioarchaeologists have recently examined the effects of diet and disease on the macroscopic surface of the skeletons but none have looked at the effects of the Great Famine on their bone microstructure. 
\nThe skeleton reacts to physiological effects of stress on the micro level before presenting as lesions on bone, and since many diseases are not reflected in the bioarchaeological record, histological analyses of bone may be informative of the lived experience of those with and without lesions. This study compares bone remodeling patterns in the ribs of 99 adults and 87 subadults between four disease categories: metabolic disease, infectious disease, comorbidities of infectious and metabolic disease, and no lesions, to determine if variation in bone microstructure exists between disease types. This was conducted using bone histological variables that reflect the maturity of remodeling and extent of porosity, which can be indicative of overall health. Additionally, carbon (δ13C) and nitrogen (δ15N) stable isotope values obtained from the ribs of the same individuals were compared to bone histological variables to determine if changes in diet due to the loss of the potato and the introduction of maize as a relief food influenced bone remodeling. 
\nThe results show that rib bone porosity is highest in adults without lesions and lowest in adults with evidence of infectious disease. Similar results were obtained in the subadult cohort. Additionally, those with evidence of scurvy generally show less porosity throughout the rib cortex for both age cohorts. Comparisons of histomorphometry with stable isotope values showed a positive correlation between osteon size and δ15N values in adults as well as a positive correlation between Haversian canal size and δ13C values in the subadults, possibly indicating slower remodeling in those with more evidence for starvation and less mature remodeling in those with maize in their diet, respectively. This study showed that bone histology is impacted by both disease and diet and may be useful for interpreting the meaning of lesions and understanding the impact of social status on population health.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.226
Teacher spread0.186 · 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
Published2021
Admission routes1
Has abstractyes

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