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Record W2582169693 · doi:10.1017/jie.2016.30

The Importance of Culturally Safe Assessment Tools for Inuit Students

2017· article· en· W2582169693 on OpenAlexaboutno aff
Jasmin Stoffer

Bibliographic record

VenueThe Australian Journal of Indigenous Education · 2017
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsDue diligenceDiligenceCulturally appropriateCultural safetyCultural diversityPsychologyEngineering ethicsMedical educationPedagogySociologyEngineeringBusinessMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

There are still no major assessment and diagnostic tools that educators can use to properly assess our Inuit students’ learning. Cultural safety as it is currently defined in New Zealand educational research (Macfarlane et al., 2007) is necessary in creating a classroom community that encourages the appreciation of culture and worldview, and ultimately enables success as defined by the culture and community of the students. Modern day assessment tools used with Inuit students must also conform to this standard of cultural safety in order to ensure the equity and authenticity of the assessment results. There is a need for ongoing research and development of culturally safe assessment tools. To date, recommendations that include collaboration with local populations, evaluation of the tools presently being used, and the due diligence of ensuring these tools are culturally unbiased are a few guidelines that have the potential of creating culturally safe assessments that portray students’ true learning abilities and assist both teacher and community in the support of their students’ learning and success.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.094
GPT teacher head0.484
Teacher spread0.391 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2017
Admission routes1
Has abstractyes

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