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Record W1863088542 · doi:10.47678/cjhe.v43i1.184237

Book review - Visualizing social science research: Maps, methods, and meaning

2013· article· en· W1863088542 on OpenAlexaffvenue
Tracy L. Durksen, Cheryl Poth

Bibliographic record

VenueCanadian Journal of Higher Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMeaning (existential)VisualizationResource (disambiguation)Process (computing)Meaning-makingSociologyData scienceComputer sciencePsychologyEpistemologyArtificial intelligence

Abstract

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Wheeldon, J. &Âhlberg, Μ. Κ. (2012). Visualizing Social Science Research: Maps, Methods, and Meaning. Thousand Oaks, CA: Sage. Pages: 205. Price: 37·95 CDN (paper). ISBN 978-1-412991-04-9Researchers can benefit from accessing resources that promote their development as purposeful producers and critical consumers of visual displays (Tufte, 2006) and the title of the book Visualizing Social Science Research: Maps, Methods, and Meaning, with its emphasis on visualization, demands the reader's attention. Visualization in research is an important topic because of the for visual displays (e.g., words, numbers, images) to evoke emotion and deepen our understanding beyond textual presentations. The per- spective we offer through this review is based on our interests, experiences, and expecta- tions surrounding the use of visuals within the fields of educational psychology research, program evaluation, and mixed methods. To begin, we offer our own visual representa- tion1 of the book with its existing links as well as (what we believe are) the missing links among the seven chapters (see Figure 1). In this review we discuss three areas of strength as well as highlight where the book does not meet its full potential: intended purpose, audience, and organization.Wheeldon and Âhlberg are to be commended for providing an accessible resource to further the discussion of the innovative topic of visualization within the research process. Research requires visualizing - a critical imaginative process which involves the (trans) formation of a mental image from an abstract idea. The authors explicitly state that the introductory text is aimed at providing a starting place for budding researchers (primar- ily upper-level undergraduate and graduate students) in a range of disciplines within the social sciences.Chapter 1 provides a general overview of the rationale, the research process, and the book. Despite the statement that sometimes the description of a book's organization with- in the preface is not used to its full potential (p. 16), Wheeldon and Âhlberg include orga- nizational details in the preface as well as in Chapter 1. The authors also make an ambitious claim - to provide both an introduction to research in the social sciences while focusing on the use of maps, graphs, and diagrams. In accordance with this claim, we found that the seven chapters can be arranged into two sections (see two shades of components in Figure 1): the research process and the use of maps within methods. While each chapter is of a digestible length and follows a similar layout, the progression assumes a reader will take a cover-to-cover approach. Yet given the proposed supplemental use of the book, it is dif- ficult to assume readers will take a linear approach. We instead expect that students may jump around as needed (according to the inter-connections displayed through Figure 1).Given the assumed purpose of enhancing the use of visuals in social science research, we expected the authors to model Tufte's (2001) graphical excellence through revealing complex ideas with clarity, precision, and efficiency. The authors briefly identify the in- fluence of Tufte on visual representations; yet fail to heed his instruction. We found both simple and cluttered displays that lack substance, progression, and aesthetic appeal. For example, the book includes simple visuals containing information more suited to an in- text list or table (e.g., Seven rules for social science research). The book does include useful examples of concept maps, particularly when related to decision-making in the research process (e.g., Are you interested in a relationship between variables or differ- ences between groups?). Yet we generally found that concept maps, though often used to display a lot of information, can overwhelm a reader if some in-text direction is not pro- vided. Ironically, one cluttered example provides a pictorial overview of the traditional features of concept maps. …

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How this classification was reachedexpand

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)0.019
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.635
GPT teacher head0.706
Teacher spread0.071 · 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 teacher head, not a consensus.

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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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