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Record W2067863820 · doi:10.5539/ies.v8n5p12

Bureaucratisation of the Teaching Profession in Decentralised Vocational Education—The Case of Slovenia, Europe

2015· article· en· W2067863820 on OpenAlexvenueno aff
Klara Skubic Ermenc, Jasna Mažgoň

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationVocational educationCompetence (human resources)CurriculumAutonomyPoliticsPolitical sciencePedagogyPublic administrationPublic relationsEconomic growthSociologyEconomicsManagement

Abstract

fetched live from OpenAlex

Since 2001, Slovenian vocational education has undergone major changes at the curricular and financing levels, particularly moving towards competence-based and open curricula and the decentralisation of responsibilities. Both tendencies have changed the role of the teacher, who has become a team worker with many new responsibilities in planning, assessment and evaluation of the pedagogical process. Reform measures are regularly evaluated, and research findings are an important source of information for reforms. In our secondary analysis of selected evaluations, we attempt to prove our thesis that the teacher’s autonomy has, despite this decentralisation, not increased in terms of better conditions for the students’ development. Instead, it has resulted in teachers becoming exhausted by paperwork, the purpose of which they do not realise due to poor training and difficult working conditions. The article proves that the potential positive outcomes of the reforms can be lost if reform instruments are abused for political and/or economic purposes, if the swiftness and content of the reforms are dictated by the European (financial) policies and particularistic political interests, and/or if the school is reformed according to the rules of the market.

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)0.007
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 score0.056
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.008
Scholarly communication0.0060.002
Open science0.0010.005
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 head0.127
GPT teacher head0.481
Teacher spread0.354 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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