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Record W2899219976 · doi:10.1177/1469540518810277

James Cairns, <i>The Myth of the Age of Entitlement: Millennials, Austerity, and Hope</i>

2018· article· en· W2899219976 on OpenAlexaboutno aff
Wei-Fen Chen

Bibliographic record

VenueJournal of Consumer Culture · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAusterityEntitlement (fair division)MythologySociologyGender studiesPolitical scienceArtPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

The Myth of the Age of Entitlement: Millennials, Austerity, and Hope. \nJames Cairns, , The Myth of the Age of Entitlement: Millennials, Austerity, and Hope. Canada: University of Toronto Press, 2017. 208 pp. \nReviewed by: Wei-Fen Chen, Chinese University of Hong Kong, China. \nAt first glance, given the title of this book, one might infer that it discusses the generational culture and lifestyles of millennials. In reality, however, James Cairns’ focus is on criticizing the structural forces that have reproduced and exacerbated social inequality, which have resulted in the plights of millennials across multiple social fields, including the workplace, on campus, and the natural environment. The book starts with a brief introduction to the myth about the millennial generation – how they are often presumed to be a group of spoiled, narcissistic, and irresponsible young adults who grew up enjoying material comfort and technological advancements unavailable to previous generations, and how they are ill-prepared for the “real world,” where all the nice things they believe they are entitled to will not be handed to them for free.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.009
Scholarly communication0.0080.013
Open science0.0010.003
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0100.003

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 head0.036
GPT teacher head0.363
Teacher spread0.327 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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