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Record W2994882198 · doi:10.3138/cjpe.67976

Creating New Stories: The Role of Evaluation in Truth and Reconciliation

2019· article· en· W2994882198 on OpenAlexvenueaboutno aff
Larry Bremner

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStatus quoCommissionEnvironmental ethicsSociologyTruth tellingPolitical scienceNatural (archaeology)Public relationsEngineering ethicsLawPsychologyHistoryArchaeology

Abstract

fetched live from OpenAlex

Abstract: This paper describes the origins of the Truth and Reconciliation Commission of Canada, with the focus on how evaluators and their professional associations can contribute to truth and reconciliation. At the professional association level, the actions that the Canadian Evaluation Society has taken in committing itself to incorporating truth and reconciliation into its values, principles, and practices are highlighted. At the individual level, evaluators are challenged to reflect on their practice. As storytellers, evaluators have been complicit in telling stories that, while highlighting the damaging legacy of residential schools, have had little influence on changing the status quo for Indigenous peoples and communities. The need to reconsider who should be telling the stories and what stories should be told are critical issues upon which evaluators must reflect. The way forward also needs to include a move toward a more holistic view, incorporating the interaction between human and natural systems, thus better reflecting an Indigenous, rather than a Western, worldview. The imperative for evaluators, both in Canada and globally, to see Indigenous peoples “as creators of their own destinies and experts in their own realities” is essential if evaluation is to become “a source of enrichment … and not a source of depletion or denigration.”

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)0.028
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.258
GPT teacher head0.497
Teacher spread0.240 · 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 designOther design
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

Citations7
Published2019
Admission routes2
Has abstractyes

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