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Record W2402182135

Critical perspectives on digital spaces in educational research : editorial

2015· article· en· W2402182135 on OpenAlexaffabout
de H.J. Lange, Naydene, Mitchell, Claudia Cláudia, Moletsane, Relebohile

Bibliographic record

VenuePerspectives in Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsCitizen journalismSanitationPovertyGovernment (linguistics)Media studiesCapeWork (physics)SociologyPolitical scienceVisual artsGeographyArchaeologyArtEngineeringLaw
DOInot available

Abstract

fetched live from OpenAlex

Globally, the digital is encroaching, reformulating, and recreating spaces in contemporary society (Kalantzis-Cope & Gherab-Martin, 2010). This is so in South Africa with the various applications of the digital having their roots in different places and spaces. Historically, we can look back to the development of portable video technology in the late 1960s in Canada. Organised through the National FilmBoard of Canada, a group of filmmakers initiated a new approach to documentary film production, engaging communities themselves in the process of film making (Rusted, 2010). The Fogo method, as it came to be called (named after Fogo Island, Newfoundland, where it was first used), was part of the Challenge for Change/ Societe nouvelle program. It brought together government, filmmakers, activists, and communities to address poverty through documentary film production and distribution (Baker, Waugh & Winton, 2010). It is worth noting that the Fogo method of participatory video was transported to South Africa in the late 1970s (Cain, 2009). This work became a significant part of anti-apartheid activism, especially in the Eastern and Western Cape (pers. comm. Lou Haysom). As Cain (2003) writes in herdoctoral study of participatory video that focuses on housing, water, and sanitation in communities in Eastern Cape.

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)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.172
GPT teacher head0.535
Teacher spread0.363 · 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 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

Citations1
Published2015
Admission routes2
Has abstractyes

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