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Record W7105731627 · doi:10.11575/prism/50710

Social Media and Critical Digital Museum Pedagogy: A Multicase Study of Institutions in Alberta

2025· other· en· W7105731627 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaPopularityDigital mediaInformal learningInformal educationField (mathematics)Social changeSocial transformation

Abstract

fetched live from OpenAlex

The pedagogical dimensions of social media have been a topic of discussion in the museum sector for nearly 4 decades (Crow & Din, 2010). Recently, however, widespread institutional closures prompted by the onset of the COVID-19 pandemic have resulted in a rapid transformation in how and why museums engage with social media. A particularly notable development has been a surge in social media adoption, reported by over 50% of institutions (International Council of Museums, 2020). Despite its increasing popularity amongst museums, uses of social media are still being shaped by continuously evolving digital platforms, neoliberal institutional imperatives, and diverse organizational priorities—factors that complicate effective and ethical social media use (Kidd, 2011). At the same time, the educational dimensions of museum social media practices remain largely underexamined and a more nuanced understanding of the informal and nonformal learning facilitated by museums’ social media engagement is critically needed. This study was driven by the following research question: How do museums and museum educators understand and engage with critical social media pedagogy? Further sub-questions included: How do museums engage in nonformal pedagogical activities on social media? What values and priorities drive these approaches? What assumptions or intentional choices about their audiences do museums make when developing content to engage with learners? Do competing priorities or interests influence museums’ approaches to social media pedagogy? How does social media facilitate informal learning about museums and their role in the community? The study was grounded in the field of Adult Education, focusing on public pedagogy and digital education. I adopted a multicase study methodology to provide in-depth insights into the phenomenon at the centre of this research by examining three large museums in Alberta, Canada. Data was gathered from three sources: online museum publications, social media content created by museums, and practitioner perspectives through semi-structured interviews and focus groups. I examined the data within a critical theoretical framework to apply a socially oriented, critical lens to practitioner and institutional perspectives surrounding this phenomenon. The findings are presented according to five tensions that emerged from the data as findings: conflicts between marketing and pedagogical objectives; balancing nonformal self-directed learning and collective learning; community-centred and authoritative pedagogical approaches; pleasing learners and critical pedagogical objectives; and quantitative or qualitative measures of success.

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)0.005
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: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0270.009
Scholarly communication0.0080.003
Open science0.0040.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.089
GPT teacher head0.435
Teacher spread0.346 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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