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Record W53915587 · doi:10.20361/g2v89k

Follow Your Money: Who Gets it, Who Spends It, Where Does it Go? by M. Hlinka

2014· article· en· W53915587 on OpenAlexvenueaboutno aff
Patti Sherbaniuk

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic, financial, and policy analysis
Canadian institutionsnot available
Fundersnot available
KeywordsProfit (economics)Index (typography)AdvertisingThe InternetBusinessCommerceEconomicsMarketingComputer scienceWorld Wide WebMicroeconomics

Abstract

fetched live from OpenAlex

Hlinka, Michael. Follow Your Money: Who Gets it, Who Spends It, Where Does it Go? Illus. Kevin Sylvester. Annick Press, New York, 2013. Print.Follow Your Money is a fairly basic yet informative examination of the economy and its complexities, aimed at older kids and teens. The book starts off with a quick synopsis of the “spider web” of the economy and a brief rundown of the history of money and the concept of profit. The author then breaks down the costs of various activities (a bus ride for example) and objects (baseball caps, sneakers), from raw materials to manufacturing costs, labour, transport, store markups, profits, etc. The author (a CBC commentator on business) is clever in what he has chosen, selecting objects and activities that are of interest to older kids and teens (computers, designer jeans, chocolate, cell phones, music, etc.). There are also a couple of pages on fuel and its importance to the economy. The book then takes a brief look at banking and the pros and cons of credit and debit cards, and then finishes with resource suggestions for additional information and an index.The cost breakdowns may be too numerous and a little dry for some readers (depending on their interest and attention level), but they are a very effective method of getting the reader to think about where things come from, how various economic factors affect prices, and who gets the profits. The author includes interesting sidebars of historical facts and trivia about particular subjects- tea, for example, or disposable bags-adding a bit of humour in the process. The illustrations are colourful and quirky and help clarify the points the author is making.Ideal for upper elementary and teen readers.Highly recommended: 4 out of 4 stars Reviewer: Patti SherbaniukPatti is a Liaison Librarian at the Winspear School of Business at the University of Alberta. She holds a BA in English and an MLIS, both from the University of Alberta. She is passionate about food, travel, the arts and reading books of all shapes and sizes.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.109
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1090.135

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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 designObservational
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
Published2014
Admission routes2
Has abstractyes

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