A New Year of New Reading: Teacher-Librarians Recommend Great New Books
Bibliographic record
Abstract
Welcome to the new issue of the Deakin Review of Children’s Literature! My name is Joanne de Groot and it is my great pleasure to introduce you to the first issue of 2013. I am an instructor in the Teacher-Librarianship by Distance Learning (TLDL) program at the University of Alberta, in Edmonton, Canada. The TLDL program is an entirely online Master of Education that prepares teachers across Canada and around the world to become teacher-librarians. The MEd in Teacher-Librarianship develops school and professional leadership in inquiry, literacies, technology, and resources through meaningful learning experiences. All the courses in this program focus on curriculum, community, consultation, collaboration, coordination, and communication. You might be wondering why I was asked to write this editorial and why I am introducing you to this issue. Let me explain! In the fall of 2012, I taught a required course in the TLDL program called ‘Introduction to Resources for Children and Young Adults’ (EDES 546). This course provides students with an introduction to literature and other print and digital media for children and young adults and provides strategies and resources for selecting and using these resources in library settings. The major assignment for this course asked small groups of students to work together to develop a proposal to build a focused collection of resources for a school library. Each group had to select a particular area or topic and then work through a series of tasks to develop their proposal. Groups in this particular class focused on topics such as Canadian materials, graphic novels, high interest/low vocabulary titles, picture books, reluctant readers, and First Nations resources. Each group had a number of required tasks to complete as part of their project, including: a selection list of resources they would purchase to build their collection; the selection criteria they used to develop this list; and links to professional reviews for some of the items on their list. The complete group projects are also available online to anyone who is interested in viewing them. Please contact me for the links. In addition, each student had to write their own professional reviews for two items on the selection list. Many of the reviews written by the students in this course have now been compiled to form the basis of this issue of the Deakin Review of Children’s Literature. When I first approached the editorial team at the Deakin Review about the possibility of having my students contribute to this issue, none of us were sure how, or if, it would work. I greatly appreciate that the entire team at the Deakin Review was willing to take a leap of faith and put this issue almost entirely into my students’ hands. The task of writing reviews that were going to be published in a reviewing journal made the assignment that much more relevant and interesting for my students. I would like to thank everyone at the Deakin Review for their enthusiastic support of the idea and for working with me and my students to make it happen! And now, without further delay, I am very pleased to share with you this new issue of the Deakin Review of Children’s Literature, brought to you by the students in the Fall, 2012 sections of EDES 546. Happy Reading! Joanne de Groot Adjunct Assistant Professor Department of Elementary Education University of Alberta email: degroot@ualberta.ca
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.241 | 0.215 |
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".