MétaCan
Menu
Back to cohort
Record W1528427055 · doi:10.32316/hse/rhe.v16i1.437

Teaching to the Test or Testing to Teach? EducationalAssessment in British Columbia, 1872-2002

2004· article· en· W1528427055 on OpenAlexaffvenueabout
Alastair Glegg, Thomas Fleming

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Practices and Policies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEducational assessmentPopularityGovernment (linguistics)Standardized testTest (biology)Scale (ratio)Educational testingPolitical scienceStandards-based assessmentPsychologyPublic relationsPedagogyPublic administrationMathematics educationLawGeography

Abstract

fetched live from OpenAlex

Over the years educational assessment in British Columbia has served many purposes in addition to recording student progress. Initially it helped provide evidence that the novel idea of a publicly funded school system was a worthwhile financial and social investment. As schooling expanded so did public examinations, ensuring that content and standards were consistent throughout the province. Between the wars educational priorities dominated assessment, as reformers challenged the validity of traditional testing and the popularity of large-scale assessment and mental testing increased. Recently schooling has become more politicized, and the purposes and methods of assessment have become subjects of public debate, often reflecting the priorities and philosophies of the government in power. Current attitudes to formal assessment appear to be determined by a combination of the factors that have influenced it over the years, and what started as a fairly straightforward concept has become increasingly complex and controversial.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.936

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0100.004
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.129
GPT teacher head0.411
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations3
Published2004
Admission routes3
Has abstractyes

Explore more

Same venueHistorical Studies in Education / Revue d histoire de l éducationSame topicEducational Practices and PoliciesFrench-language works237,207