<i>Mapping Recreational Literacies: Contemporary Adults at Play</i> by Margaret Mackey
Bibliographic record
Abstract
Sometimes you read a book that makes you feel genuinely excited about your vocation.These are books that matter.Mapping Recreational Literacies: Contemporary Adults at Play by Margaret Mackey falls into the category of books that matter.The book let me take stock of the articles, chapters, and books that I have read over the past few years, and it extended how I feel about them, the field of multimodality and "new" literacies more generally.As Turkle (2007) observes, "We think with the objects we love; we love the objects we think with" (p.5).Mackey lets us see how texts make us learn in varying ways while at the same time analyzing what it is about them that absorbs our attention.With eloquence and measured analysis, Mackey offers a detailed picture of nine individuals and their textual worlds and how they interact with other textual worlds.The book serves as compelling evidence of how much we think and exist through objects in our worlds.To keep the concept of mapping fluid, Mackey structures her book around genres of texts and participants' relationships to these genres.She artfully explores this landscape of textual networks by mapping (note the double meaning here) the stories of Ben, Courtney, Seth, and others and the tacit principles of their meaning-making onto the text content and design.Each of the nine meaning-makers carries his or her own unique cultural agency that is foregrounded with particular texts, and the case studies work well as a collective in discussion/implications sections.In the book Mackey takes account of the world of games, of picture books, of novels, of graphic stories-and she does so not cursorily, but rather fixes her gaze on what these texts do and, to return to Turkle, how we think with texts that we love.By invoking Rabinowitz's four rules of reading, Mackey shows us that there are ways of connecting other research frameworks to multimodal theory.Mackey describes the "distinctive individuality of each participant," and like Mackey, I would have expected some repetition in responses, but Mackey's thick description of their textual worlds teased out how different their worlds were.Jennifer Rowsell is an assistant professor in English education in the Graduate School of Education where she teaches undergraduate and graduate courses in literacy education.She has co-written books and articles in the areas of new literacy studies, multimodality, family literacy, and multiliteracies.She is involved in three research studies: one looking at adolescent artifacts as a way into writing; an ARC-funded study in Australia and the United States on parents' networks of information about literacy and literacy development; and research on the production of print and digital media as the basis of a pedagogy of innovation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".