MétaCan
Menu
Back to cohort
Record W4389190087 · doi:10.1515/libri-2021-0098

Memes to an End: Why Internet Memes Matter to Information Research

2023· article· en· W4389190087 on OpenAlex
Bonnie Tulloch

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

VenueLibri · 2023
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThe InternetSociologyNegotiationReading (process)Field (mathematics)EpistemologyInformation literacyEmpirical researchComputer scienceWorld Wide WebSocial sciencePedagogyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract This theoretical paper explores the significance of Internet memes to the field of information research. Adopting a constructivist framework, it conceptualizes memes as documents that undermine popular assumptions about people’s engagements with information. In particular, it argues that Internet memes are conceptual tools through which people can negotiate different representations of reality and the logics that underlie them. Through a close reading of several memetic examples, I propose that memes are a means through which Internet users document and test their values against those of others, thereby allowing them to explore the different courses of action associated with situations they encounter. Memetic communication is thus presented as an important new information literacy practice that has critical implications for the following research areas: education, freedom of expression, ethics and policy, and the preservation of cultural heritage.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.996

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.058

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 head0.117
GPT teacher head0.457
Teacher spread0.340 · 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