MétaCan
Menu
Back to cohort
Record W2751981578

Video, Art, and Dialogue: Using the Internet to Create Individual and Social Change

2013· article· en· W2751981578 on OpenAlexaff
Pamela Snell

Bibliographic record

VenueThe International Journal of Communication and Linguistic Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe InternetThe artsIdentity (music)SociologyConsciousnessInterpersonal communicationQueerDigital artInternet privacyPsychologyPublic relationsSocial psychologyAestheticsComputer scienceWorld Wide WebVisual artsCommunicationPolitical scienceGender studiesArt
DOInot available

Abstract

fetched live from OpenAlex

This paper demonstrates that arts-based learning, when combined with digital technology, can foster community development and identity construction. By using the internet to build artistic dialogues that transcend time and distance, relationships are undertaken with people we might not otherwise encounter and supportive online communities can be established. The internet has provided a forum for marginalized individuals to find their voices, a place where identities can be fluid and words can be witnessed. Therefore, when integrated into a structured arts-based learning environment, digital technologies can lead to greater understanding and acceptance of one's own identities. Using the innovative model established in the pilot project Queer Connections as the primary example this paper offers insight into how levels and layers of communication in human interactions may influence societal change, how collective creation can be used to access deeper levels of understanding and critical consciousness, and how arts-based interpersonal learning can be used as a catalyst to foster community development and identity construction.

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.008
Scholarly communication0.0070.007
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.729
GPT teacher head0.629
Teacher spread0.100 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe International Journal of Communication and Linguistic StudiesSame topicParticipatory Visual Research MethodsFrench-language works237,207