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Collective Intergenerational Responsibilities

2023· book-chapter· en· W4366494108 on OpenAlexaboutno aff
Michael Sullivan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSovereigntyPoliticsPolitical scienceEconomic JusticeState (computer science)Corporate governanceCriminal justiceWelfareGovernment (linguistics)Political economyImmigrationSociologyCriminologyPublic administrationLaw

Abstract

fetched live from OpenAlex

Abstract In this chapter, I ask how the current generation should accept responsibility for the injustices of its forebears in a way that helps all citizens to progress towards reconciliation. Here, I address the challenge of reconciliation with First Nations given the role of settler states, including Canada and the United States, in intentionally destabilizing Indigenous communities and families through the residential school system, child welfare interventions, and the criminal justice system. Here, I argue for three linked responses to the intergenerational legacy of policies that separated Indigenous families and destabilized their communities and political life. The first involves building immigrant-settler-Indigenous alliances. The second involves the government’s responsibility to avoid perpetuating Indigenous family separation and community destabilization through its criminal justice and child welfare policies. The third involves strengthening Indigenous political sovereignty by expanding their self-governance, participation, and free movement rights across their territories divided by settler state borders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0050.004
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0200.003

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 head0.045
GPT teacher head0.320
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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