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Record W4239977616 · doi:10.32920/ryerson.14646786.v1

Exploration of affordances in subscription-based digital magazines

2021· preprint· en· W4239977616 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAffordanceCategorizationVariety (cybernetics)Computer scienceOrder (exchange)EntertainmentMultimediaHuman–computer interactionWorld Wide WebVisual artsArtificial intelligenceArtBusiness

Abstract

fetched live from OpenAlex

This study analyzed 128 digital magazines through the lens of affordance theory in order to analyze the current state of digital publishing and establish a framework for the design and development of digital magazines. Twenty affordances were identified and categorized into four distinct groups: extend content, community involvement, utility, and entertainment. Overall, a nonlinear relationship between the number of digital subscriptions and the variety of affordances implemented in a magazine was identified. Additionally, the 20 affordances identified were analyzed against three previously established frameworks, including Gibson’s original categorization of perceived, hidden, and false affordances. This study provides valuable information for the media industry regarding the application of the theory of affordance and how it applies to digital magazines. In order for a digital magazine to be perceived as a successful adaptation of the print issue, it must provide the end user with a unique and immersive experience

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.274
Teacher spread0.231 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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