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Record W3115675913 · doi:10.35502/jcswb.165

Our Shared Future: Windows into Canada’s Reconciliation Journey — A Review

2020· review· en· W3115675913 on OpenAlexaffvenueabout
Peter Shipley

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2020
Typereview
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsGeorgian College
Fundersnot available
KeywordsIndigenousCommissionPolitical scienceAction (physics)Prime ministerLawSociologyPublic administrationPolitics

Abstract

fetched live from OpenAlex

The challenges and complexity of the reconciliation process are still not well understood by a large number of non-Indigenous people in Canada. As a nation, we are attempting to grasp the intricacy of how to unravel and atone for the damage that has been done in establishing and managing the more than 130 residential schools in Canada. This not only impacted more than 150,000 First Nations, Métis, and Inuit children but destroyed generations of families that are still and will continue to be impacted for years to come. The official apology from Prime Minister Stephen Harper on June 11, 2008, to all Indigenous people in Canada for the atrocities of the Indian Residential Schools was the start of a very long and painful continuous journey. The 94 calls to action released in 2015 by the Truth and Reconciliation Commission provide a road map to a complex recovery process for Indigenous people across the country. In January 2018, Health Canada held a national panel discussion with Indigenous leaders and experts on the question “Reconciliation—What Does it Mean?” One of the main themes of reconciliation revolves around education, and, in order to stay focused, we must continue to educate Canadians, including police leaders and new recruits, as we move through the meandering path of econciliation. The book Our Shared Future provides an outstanding in-depth look through the windows into a number of individual perspectives on the reconciliation journey.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.012
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 head0.087
GPT teacher head0.433
Teacher spread0.347 · 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
GenreReview

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
Published2020
Admission routes3
Has abstractyes

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