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Record W2788716829 · doi:10.14742/ajet.3817

Social media use by instructional design departments

2018· article· en· W2788716829 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAustralasian Journal of Educational Technology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsRoyal Roads University
FundersCanada Research Chairs
KeywordsSocial mediaField (mathematics)Computer scienceGraduate studentsContent analysisPsychologyMedical educationWorld Wide WebSociologyPedagogySocial science

Abstract

fetched live from OpenAlex

The aim of this investigation was to gain an understanding of the use of institutional social media accounts by graduate departments. This study focused particularly on the social media accounts of instructional design (ID) graduate programs. Content and statistical analyses were conducted to examine 24,948 tweets posted by ID programs (n = 22) on Twitter. Results revealed that ID graduate programs primarily used Twitter to broadcast resources and materials related to the field. Additionally, results showed that ID programs most frequently used Twitter to boost the profile of their program. Yet, tweets highlighting student and faculty accomplishments had the highest percentage of community interactions (likes and retweets). These findings suggest that ID programs are functioning as filters of information relevant to the field rather than conversational hubs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.363
Teacher spread0.320 · 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