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Record W4390729887 · doi:10.12681/edth.36364

Personal and professional development through sustainable partnerships in education Serbian experiences from the I-TAP-PD Erasmus+ project

2023· article· en· W4390729887 on OpenAlexaff
Nikola Koruga, Dunja Đokić, Sanja Krsmanović Tasić

Bibliographic record

VenueΕκπαίδευση & Θέατρο · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Science and Water Management
Canadian institutionsASTER
Fundersnot available
KeywordsGeneral partnershipErasmus+SerbianEmpathyContext (archaeology)Thematic analysisPedagogyAction researchProfessional developmentMedical educationQualitative researchPersonal developmentPsychologySociologyMedicinePolitical scienceArtSocial scienceGeographySocial psychology

Abstract

fetched live from OpenAlex

The aim of this research is to evaluate how the partnership between teachers and artists was built in the context of Serbia. In conducting it, we sought answers to the following research question: What was the role of I-TAP-PD in the personal and professional development of teachers and artists in Serbia? It is based on the phenomenological theory of reflection in action. Research participants include three teacher-artist pairs. The residencies were delivered between August 2021 and June 2022. The data was collected with pre-residency questionnaires, reflective journals, classroom observation, post-residency group interviews. All qualitative data was analysed using thematic analysis. The results show that artists and teachers, in terms of sustainable partnership in researched practices in Serbia, identified the understanding of the “other“ as the most important aspect of the process of learning and teaching. In this endeavour, professionals developed high levels of empathy and their self-esteem notably increased.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.007
Scholarly communication0.0050.002
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.033
GPT teacher head0.277
Teacher spread0.243 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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