My Literacy Adventure with People, AI, and a Piglet
Bibliographic record
Abstract
This work is a "multiliteracy narrative," describing my history of literacy development from four perspectives: Japanese, English, digital literacy, and global citizenship. The main focus of this paper is how I overcame the challenge of acquiring high language skills through interaction with the people around me. Also, I explore how literacy skills can empower me to survive in today’s world. Firstly, this narrative starts with my literacy development in my native language (Japanese), which began at a young age when my parents started reading me a book. As I entered elementary school, my love for reading deepened. Even though I struggled to write compositions, a dedicated teacher encouraged me to overcome this challenge. Secondly, I explain how I developed an interest in English. I enjoyed having a chitchat with a teacher from Canada in my elementary school. My English teacher in high school guided me through academic reading and writing. I had a pivotal moment when another teacher reminded me of the true purpose of learning English. In addition, I share how guidance from my parents about online communication and the importance of empathy became the foundation of my digital literacy. At the same time, I introduce a lesson from my history teacher, which convinced me why information literacy is crucial in the digitalized world. Finally, I analyze the two definitions of global citizenship. I discuss how these literacy skills enable me to understand the interconnectedness of the world, which is an essential quality of a global citizen.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".