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Record W7083198681 · doi:10.29173/css98

Pow! Zap! Wham! Creating Comic Books from Picture Books in Social Studies Classrooms

2002· article· en· W7083198681 on OpenAlexvenueaboutno aff

Bibliographic record

VenueCanadian Social Studies · 2002
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsComicsSocial studiesWonderPopular cultureComic stripFace (sociological concept)CartoonistNarrative

Abstract

fetched live from OpenAlex

Students' documented lack of interest in social studies has led to many attempts by teachers to make school learning more relevant to the lives of their learners. This article demonstrates two ways to deal with that apathy: First, the use of picture books is encouraged and second, a popular culture format, the comic book, is advocated as a method for students to use to illustrate their learning. We provide suggestions on how to help students create their own comic books to demonstrate their learning of social studies content gained in part through study of picture books. Specific instructions are given about how to create comic books and a student example of a comic book showing his understanding of Inuit culture is featured. Having a sense of been there, seen that, I can never resist a smile when I read popular Canadian story-teller Robert Munsch's book Thomas ' Snowsuit (1985). I've seen some dreadfully ugly snowsuits during my time in Canada, but it didn't take me long to recognize that, at least in my life, warmth takes precedence over fashion. Despite my mirth, I find I cannot help but wonder if there really is much to laugh about when enduring those long, cold Canadian winters. There have been times when I've opted for something akin to three warm snowsuits, three warm parkas, six warm mittens, six warm socks and one pair of very warm boot sort of things called mukluks! (Munsch 1986). It is a special kind of person who can face those frozen Arctic breezes with a smile on the face. Life in Canada's North Country presents all types of difficult challenges--especially when one considers the additional threat of the mythical Qallupilluit wanting to drag little children through cracks in the ice (Munsch Kusugak 1988)! One cannot help but be filled with admiration for the Inuit people who for so long have endured Canada's seemingly endless winters, struggling against the elements while retelling legends that help to explain their surroundings.

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)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.105
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0060.006
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1050.040

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 head0.064
GPT teacher head0.269
Teacher spread0.205 · 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
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
Published2002
Admission routes2
Has abstractyes

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