Marketing Strategies of Tuku Vocational High School’s (TKVS) Library in Taiwan
Bibliographic record
Abstract
Traditionally, rather than using books in the library, a typical Asian student preferred reading and studying from textbooks in order to pass the college entrance examination. Likewise, this trend was the same in the small suburban town in Taiwan like Tuku with the area is 46 square kilometer, its population of 32,000 people, and 70% of its population is farmers. Due to its agriculture background, there are only two libraries to service its residents. One is the public high school’s library that belongs to Tuku Vocational High School that has approximately about 28 classes and 1000 students while the other is Tuku’s public library. Owing to the textbooks edition from one tomany, the exam content has changed a lot from the past to the present. In order to master the examination, multiple facets of literacy should be mastered. Because of this, library should be gaining popularity than before. In contrary, this was not the case. Library books were seldom checked out according to the electronic tracking system for library books. From three years ago, the average of books checked out was below 15 per day. That average has steady climbing to about 120 with the maximum around 370. Recently, by conducting an open-end questionnaires and chatting with its students, it was found that some marketing strategies are needed to educate them the importance of utilizing the books in the library as the primary source of mastering multi-facets of literacy instead of textbooks.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".