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Record W2340178350 · doi:10.1177/135945751402800102

What Does the Past Tell Us? A Content Analysis of the First Quarter-Century of the <i>British Journal of Music Therapy</i>

2014· article· en· W2340178350 on OpenAlexaboutno aff
Giorgos Tsiris, Neta Spiro, Mercédès Pavlicevic

Bibliographic record

VenueBritish Journal of Music Therapy · 2014
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsMusic therapyDisciplineQuarter (Canadian coin)Content analysisSociologyLibrary scienceSocial scienceMedia studiesPsychologyHistoryPsychotherapist

Abstract

fetched live from OpenAlex

Professional journals have a legitimating and sanctioning role in the development of disciplinary knowledge, as well as professional practices and identities. The British Journal of Music Therapy (BJMT) – the only UK-based peer-reviewed music therapy journal – has portrayed research, theory and accounts of practices, reflecting trends and developments in the field of music therapy since 1987. Marking the 25th anniversary of the BJMT and looking into its future development, a content analysis of the journal since its inception (1987–2011) was conducted with the aims of (i) tracing trends and developments of music therapy praxes and professional identities, and (ii) exploring the journal's engagement with disciplinary discourses and practices alongside and beyond those of music therapy. The study provides an overview of the BJMT in terms of 1) paper types, 2) authorship: numbers and professional titles, 3) countries of project sites and countries of authors, 4) sample conditions, sizes and ages, 5) formats of practices, and 6) models and themes. The results show that the majority of the articles published in the BJMT are theoretical, focus on one-to-one sessions, are single authored by music therapists and are UK-focused in terms of authorship, project site and models. This study brings to the fore questions for the future development of music therapy as profession and discipline.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.270
Teacher spread0.235 · 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 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

Citations11
Published2014
Admission routes1
Has abstractyes

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