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Record W2973158504 · doi:10.11575/prism/36974

Current and Emerging Trends Influencing Educational Research - Document Scan - Themes

2017· article· en· W2973158504 on OpenAlexaboutno aff
Cameron Smith

Bibliographic record

VenuePRISM (University of Calgary) · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Overview: This summary document presents brief synopses of seven themes related to current Canadian and international education, prepared for the Werklund School of Education Research Areas Working Group. Listed alphabetically, these themes connect to current and emerging trends influencing educational research in early years, K-20, and adult education. These themes were drawn from a scan of over 70 reports and research articles from international education bodies (e.g. OECD, UNESCO), research networks (e.g. Galileo Educational Network), organizations (e.g. McKinsey & Company), and Canadian Ministries of Education (Ontario and Alberta). These themes are: • Adult Education & Lifelong Learning • Belonging & Inclusion • Citizenship & Changing Societies • Digital Technology & STEM • Engagement & Retention • Health & Wellbeing • Literacy & Numeracy.

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)0.093
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.491

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.104
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0240.061
Science and technology studies0.0050.008
Scholarly communication0.0190.016
Open science0.0030.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.002

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 head0.115
GPT teacher head0.421
Teacher spread0.306 · 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.

Study designNot applicable
DomainEvaluation
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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