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Record W7099236764

Principles for Fair Student Assessment Practices for Education in Canada The Principles for Fair Student Assessment Practices for Education in Canada contains a set of

2011· article· en· W7099236764 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsSet (abstract data type)Process (computing)Standards-based assessmentCommissionConstruct (python library)Educational assessmentCurriculumProfessional associationBest practice
DOInot available

Abstract

fetched live from OpenAlex

principles and related guidelines generally accepted by professional organizations as indicative of fair assessment practice within the Canadian educational context. Assessments depend on professional judgment; the principles and related guidelines presented in this document identify the issues to consider in exercising this professional judgment and in striving for the fair and equitable assessment of all students. Assessment is broadly defined in the Principles as the process of collecting and interpreting information that can be used (i) to inform students, and their parents/guardians where applicable, about the progress they are making toward attaining the knowledge, skills, attitudes, and behaviors to be learned or acquired, and (ii) to inform the various personnel who make educational decisions (instructional, diagnostic, placement, promotion, graduation, curriculum planning, program development, policy) about students. Principles and related guidelines are set out for both developers and users of assessments. Developers include people who construct assessment methods and people who set policies for particular assessment programs. Users include people who select and administer assessment methods, commission assessment development services, or make decisions on the basis of assessment results and findings. The

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.135
GPT teacher head0.466
Teacher spread0.331 · 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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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