The Effect of “The Book Characters Are Among Us” Activity on the Reading Culture of Students
Bibliographic record
Abstract
We live in an era where knowledge becomes very diverse, expands and change quickly. Reading is the most effective way in order to follow the knowledge, which is accelerated by the democracy and developments of the communication technologies. Therefore, modern societies give special attention to the activities regard to the reading culture. Nowadays, we are facing a young generation which does not need and care about reading as well as does not know how/why to read a literature writing. This generation prefers a consumption based artificial game world which is served via computer technologies and this kind of artificial places that internet cafes and game centers serve rather than reading and going to libraries. This study aims to present “Book Characters Are Among Us” activity which makes reading interesting for teenagers by connecting reading with drama and improves their reading, writing and speaking skills. In addition, it evaluates the results of the activity which was held with a group of students. It was observed that, students started being interested in reading books, doing critical reading to understand and know the characters in the books, obtaining the creative thinking and writing experience and improving their speaking abilities that’s to “Books Are Among Us” activity. The activities were intentionally organized in Z Library in order to make students be aware of the facilities and possibilities which were given to their usage. The factors such as comfortable sitting zones, easiness of reaching a book, computer technology, being wide and spacious, usage of the place for book interviews, presentations and drama activities other than book taking and returning are the reasons for students to come to Z Library.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".