Pembuatan Komik Literasi Informasi Untuk Meningkatkan Literasi Siswa di Perpustakaan SMA Negeri 1 Padang
Bibliographic record
Abstract
AbstractThe writing of this paper aims to explain the making of information literacy comics to increase student literacy in the Public Library of Padang 1 High School. The method used in this paper is descriptive method, the data is taken through field observations and interviews with librarians and librarians, based on facts that occur in the field of Public Library 1 Padang. Based on these results it can be concluded that the making of information literacy comics can be concluded the following steps. Determine the topic or theme of the Comic, Thinking of characters or extras, Determine the character to be played, Determine the setting of the place, Determine the setting of the atmosphere, Determine the setting, Determine the title, Make a title, Make a script, Provide tools and materials, Make a comic panel, Make a sketch of a picture comic characters, thickening comic character drawings, sketching conversation balloons, thickening panel lines, making storyline text columns according to narration, coloring sketch pictures on comics, thickening story conversation balloon lines, filling text in conversation bubbles and storyline columns in comics, Thicken the character image using a ballpoint pen. Keywords: comics, information literacy comics, student literacy
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".