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Record W2953522274 · doi:10.29333/iji.2019.12349a

Elementary Teacher’s Perceptions of Education Reforms in Albania

2019· article· en· W2953522274 on OpenAlexaff
Enkeleda Arapi, Frédèric Lasserre

Bibliographic record

VenueInternational Journal of Instruction · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicBalkans: History, Politics, Society
Canadian institutionsUniversité LavalUniversité de Montréal
Fundersnot available
KeywordsModernization theoryCurriculumCommunismPoliticsUnderpinningQualitative researchPedagogySociologyPerceptionTeacher educationMathematics educationPolitical sciencePsychologySocial science

Abstract

fetched live from OpenAlex

After the fall of the communist regime in 1991, Albania undertook a profound programme of reform to its education system, along similar lines to those reforms being undertaken elsewhere in Central and Eastern Europe.These reforms were political, structural, and pedagogicalaltering both the curriculum as well as teaching methods.This article intends to analyse the manner in which these hugely ambitious reforms were received by teachers in Albania, emphasising that it is ultimately those aspects that affect their daily working lives which have caught their attention.The research is based on a qualitative approach discourse analysis of interviews conducted with a sample of 12 teachers in elementary schools in the Tirana region.The results underline the fact that teachers have a fairly accurate understanding of the modernization objectives of the reform; they are positive about the changes to the curriculum and to teaching methods, but their opinions diverge when it comes to the process of choosing textbooks.However, participants appeared to be barely able to remember the political and structural drivers underpinning the reforms.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.325
Teacher spread0.314 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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