Book review - Visualizing social science research: Maps, methods, and meaning
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".