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Record W2007034446 · doi:10.14507/epaa.v23.1752

Are Teachers Crucial for Academic Achievement? Finland Educational Success in a Comparative Perspective

2015· article· en· W2007034446 on OpenAlexaff
Eduardo Andere

Bibliographic record

VenueEducation Policy Analysis Archives · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsYork University
Fundersnot available
KeywordsPerspective (graphical)Student achievementQuality (philosophy)Mathematics educationProcess (computing)Academic achievementTeacher qualityPolitical sciencePsychologyPedagogySociologyBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

Teachers are seen as the main reason behind the high, equal, and consistent student performance in Finland as measured by the Programme for International Student Assessment (PISA), and there is a lot of truth in this. Candidates for teacher training programs are selected through a rigorous process, for example. However, using primarily the case of Finland, this paper seeks to show that factors beyond the quality of teachers are also involved in explaining high performance on international standardized tests by students around the world. The policy of attracting high-caliber students and providing high-quality preservice training, suggested by organizations such as the Organisation for Economic Cooperation and Development (OECD) and McKinsey & Company, does not necessarily seem to be related to high student performance in all countries.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.004
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.079
GPT teacher head0.470
Teacher spread0.392 · 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 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

Citations35
Published2015
Admission routes1
Has abstractyes

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