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Record W3008783731 · doi:10.22329/csw.v16i1.5914

Reading World Social Work Day 2010 through Multi-modal Analysis

2019· article· en· W3008783731 on OpenAlexaffvenue
Tara La Rose

Bibliographic record

VenueCritical Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsTrent University
Fundersnot available
KeywordsNarrativeFraming (construction)Social mediaDigital mediaReading (process)SociologyMedia studiesComputer scienceMultimediaLinguisticsWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

This paper considers World Social Work Day 2010, a digital media story created and shared on YouTube by the International Federation of Social Workers. Multi-modal analysis is used to reveal the multi-vocal nature of the text and to consider the particular social work discourses used in this digital narrative. Connections between and across digital texts are used to explore the capacity of digital media to produce multiple meanings and to allow texts to exist in multiple contexts simultaneously. Post-structural perspectives framing this analysis encourage consideration of the potential meanings of globalization in social work, particularly when narrative texts are shared via online digital media environments and interpreted by both intended and unforeseen audiences.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.014
Science and technology studies0.0080.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.004

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 head0.064
GPT teacher head0.418
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; both teacher heads agree on what is shown here.

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

Citations1
Published2019
Admission routes2
Has abstractyes

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