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Record W4313586274 · doi:10.3389/feduc.2022.719107

Okiskinwahamâkew: Reflecting on teaching, learning and assessment

2023· article· en· W4313586274 on OpenAlexaffabout
Patricia J. Steinhauer

Bibliographic record

VenueFrontiers in Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Communication, and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIndigenousPrivilege (computing)IndigenizationSociologyStandardizationValue (mathematics)LiteracyPedagogyMathematics educationEngineering ethicsComputer sciencePsychologyEngineeringPolitical scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

This paper looks at assessment views held by Alberta Education in regards to teaching and learning for educators in Alberta. The standardization model of teaching and assessment excludes Indigenous thought systems articulated through rigorous thought processes in the nehiyaw mâmitoneyihcikan – the Cree mind and intelligences. Infusion, integration, indigenization models that privilege the dominant educational design continue to perpetuate an invisible colliding space that impacts the Indigenous thinker and learner. Privileging Indigenous language thought systems that are rich in multidimensional processes are presented to address current notions of teaching and assessment. Looking through the lens of the Indigenous language system and addressing the politics of literacy uncovers nehiyaw mâmitoneyihcikan – the Cree mind. This rich thought system reveals a sophisticated system that operates omni and multidimensionally from and within a compassionate mind – a value based way of seeing and engaging. Honoring nehiyaw thought systems, processes of coming to know and respecting Indigenous understandings of teaching and learning, lead to considering the rigorous nehiyaw understanding of okiskinwahamâkew – Indigenous informed teaching guide.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
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 head0.054
GPT teacher head0.468
Teacher spread0.414 · 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 designNot applicable
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

Citations0
Published2023
Admission routes2
Has abstractyes

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