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Record W4413095342 · doi:10.11647/obp.0477.07

7. Some Adverse Effects of Historical Trauma on Indigenous Communities

2025· book-chapter· en· W4413095342 on OpenAlexaff
Mukesh Eswaran

Bibliographic record

VenueOpen Book Publishers · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousHistorical traumaShadow (psychology)ColonizationIdentity (music)MedicinePsychologyHistoryCriminologySocial psychologyGeographyPsychotherapistEcologyAestheticsArchaeology

Abstract

fetched live from OpenAlex

Chapter 7 presents an investigation of the deleterious effects of historical trauma on Indigenous wellbeing. De facto, psychological pain shifts the weight of identity from the ‘Us’ component to the ‘I’ component. One of the effects of historical trauma is the destruction of the traditional means of pain treatment. In their absence, by diverting resources to pain alleviation, an increase in historical trauma reduces the individual contributions of Indigenous community members to collective activities. This leads to an inferior equilibrium where family and community outcomes are worse in the short run. In the long run, the reduced communal contributions erode the sense of belonging that characterizes many Indigenous communities. This further worsens outcomes. The model shows how and why the causal effects of historical trauma are durable: they do not diminish over time. It is seen that high levels of historical trauma can lead an Indigenous community to get stuck in a ‘bad’ equilibrium in which individual, family, and community outcomes are extremely compromised. The model reveals why colonization casts a long shadow and why Indigenous communities still have to struggle with the consequences of past events. Added to these outcomes are the effects of the ongoing colonization that undoubtedly exists but which the model is somewhat less equipped to formally capture.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.906
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
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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