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Record W2589465722 · doi:10.29173/cais732

Multimedia: Myths and Realities

2013· article· en· W2589465722 on OpenAlexaffvenueabout
Andy Large, Jamshid Behesti, Alain Breuleux, André Renaud

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsMcGill University
Fundersnot available
KeywordsContext (archaeology)MultimediaAnimationRecallVariety (cybernetics)Computer scienceProduct (mathematics)World Wide WebPsychologyCognitive psychologyArtificial intelligenceHistory

Abstract

fetched live from OpenAlex

From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.Multimedia products are now widely available on a variety of platforms, and there is a widespread assumption that the addition of still images, animation and sound to text will enhance any information product. The research reported in this paper investigates such claims for multimedia in an educational context and for a specific user group: grad-six primary school students. The students' ability to recall, make inferences from, and comprehend articles presented to them in print, as text on screen, and in mutlimedia format has been mesured. The findings to date suggest that the impact of multimedia is subtle, and that generalisations about the effectiveness of multimedia, at least with children in an educational context, should be employed cautionously. The long-term goal is to identify design criteria which can be employed in the production of multimedia products for schools.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0060.047
Scholarly communication0.0150.017
Open science0.0010.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0090.001

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 head0.027
GPT teacher head0.278
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2013
Admission routes3
Has abstractyes

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Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicMultimedia Communication and TechnologyFrench-language works237,207