Bibliographic record
Abstract

 
 
 Including award-winning literature in children’s library collections is often openly stated in a library’s collection development policy. Hateley (2012) notes these “meaningful markers” as a way “to grant our wish of someone somewhere, somehow having read all the books, and worked out which one is best” (p. 190). In an age where librarians are pushed to their limits with time, budget, and curriculum, such designators are useful in helping to develop and maintain a quality collection. At the same time, Hateley (2012) enlists readers to acknowledge the unavoidable human subjectivity involved in the judging process of literary book awards:
 What must not be forgotten, however, is that this superhuman work is undertaken by humans—passionate and knowledgeable humans, to be sure, but humans nonetheless. To automatically rely on award winners for collection development may mask the necessary fallibility and idiosyncrasies of individual judges or judging panels. (p. 197)
 In a study of “Children’s-Choice” State Book Awards in the US, Storey (1992) further notes censorship issues associated with the selection of books on the award lists and, thus, the availability of books to the children readers meant to select the winners. Storey’s (1992) research reports on a survey of school librarians about censorship related to these book awards. The librarians in the study noted that censorship was “expected and accepted” (Storey, 1992, p. 1). They also supported the use of award lists for selection and collection development which is the focus of the current study reported in this paper. Specifically, the purpose was to investigate youth librarians’ perceptions of using award lists for collection development and to also survey their collections for the presence of five children’s book awards.
 
 
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".