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Record W2817898698 · doi:10.18662/rrem/47

Normative and Institutional Frameworks for the Functioning of Supervision in Social Work

2018· article· en· W2817898698 on OpenAlexaboutno aff
Elena Unguru, Antonio Sandu

Bibliographic record

VenueRevista Romaneasca pentru Educatie Multidimensionala · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsNormativeFormative assessmentTrainerSupervisorNoticeWork (physics)Social workProfessional developmentSociologyField (mathematics)Process (computing)Public relationsEngineering ethicsPedagogyPolitical scienceLawEngineeringComputer science

Abstract

fetched live from OpenAlex

Supervision is understood as a professional, formative, administrative and managerial practice provided by an experimented professional to a professional in the same field, with the purpose of transfering knowledge and training specific competences, useful in his practice with the purpose of providing as qualitative as possible services to its own beneficiaries. The article reviews a series of normative frameworks based on which the process of supervision of social services in countries such as Singapore, New Zeeland, Australia, Canada, USA, Great Britain and Romania. By comparatively analyzing these frameworks, we noticed that generally, there are two models formulated – the first one which regards the supervisor as a professional with experiece in social work, and the other model sees supervision as a distinct profession with transdisciplinary nature, but with access limited by the need for an initial training and previous experience in the field of social work. We notice that, in general, the national frameworks identify three functions of supervision: administrative, formative and managerial, and place a special emphasis on the role of the supervisor as trainer in the field of professional ethics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.356
Teacher spread0.320 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations16
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueRevista Romaneasca pentru Educatie MultidimensionalaSame topicSocial Work Education and PracticeFrench-language works237,207