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Record W4411707638 · doi:10.1177/15274764251348596

Deinfluencing TikTok During the Cost-of-Living Crisis: Neoliberal Logics of (Over)Consumption Across Popular Media

2025· article· en· W4411707638 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

VenueTelevision & New Media · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsConsumption (sociology)Reality tvMedia consumptionAdvertisingNeoliberalism (international relations)BusinessEconomicsMedia studiesPolitical economySociologySocial science

Abstract

fetched live from OpenAlex

Following the economic recession of 2008, media texts blamed individual consumers and their reckless and wasteful consumption of designer goods and extravagant homes for contributing to the financial crisis. Within the current context of the aftermath of the COVID-19 pandemic, the cost-of-living crisis has led to increases in the cost of everyday necessities. At the same time, social media influencers promote excessive consumption of expensive viral products that are often discarded upon purchase. Responding to these socio-cultural events, deinfluencing became a viral trend on TikTok, where users post videos encouraging viewers to purchase certain products rather than other more expensive options. Positioning deinfluencing as an example of cultural responses to financial crises, this article highlights the parallels between deinfluencing and previous discursive articulations that emerged during the 2008 global financial crisis. Deinfluencing reproduces longstanding cultural formations of consumer citizenship while also reacting to the overconsumption promoted by the digital economy.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.281
Teacher spread0.239 · 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