Bibliographic record
Abstract
Information Literacy is built on the idea that when we encounter information we can evaluate that information to incorporate into our knowledge schema. As such information can be encountered in a variety of ways, as academic information, workplace information, or everyday life information. Art forms can also be considered information, including literature. As an art form literature has been theorized to be a window, mirror, and a sliding glass door (Bishop, 1990) to the reader, an information source regarding our world. The notion that fiction is an information source is not particularly considered in much of the information literacy scholarly research. This paper examines how adolescents engage with fiction as a source of information. Using a small case study of a class of 16 and 17 year olds the paper examines how they construct ficiton and aesthetic reading as an information source, particularly using the metaphor of the window and the mirror. While students might consider reading as a way to explore their identity, elements related to their stance towards reading impacted their ability to see reading fiction as an information source. Furthermore they were unlikely to engage fiction as a "window" or a way to learn about others. Specific pedagogical structures may encourage a more critical stance towards aesthetic reading as a way to engage in as a learning object.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".