Bibliographic record
Abstract
"Does anyone still listen to CDs?" The presentation aims to introduce the operation of our Music Collection by providing qualitative and quantitative data linked to loans, library visits, online repositories with music content, music programs and workshops. Fundamentally, our Music Collection has a value-preserving function linked to processing, preserving, and presenting the documents of local music life in a spectacular form. Virtual exhibitions based on digitized documents, various databases, and knowledge repositories are good tools for organizing knowledge. People can also be introduced to these contents during library programs (e.g., lectures, sessions, and competitions). Due to the three pillars – knowledge organization, preservation of value, and community building – our Music Collection is a place where people return to listen to music on-site in a comfortable armchair while reading their favourite magazine. Due to the ever-changing world, we must apply innovative approaches, react proactively, and adapt to changes (an example of this could be our Creative Studio). The main purpose of this study is to describe the music-related activities of a particular library (Katona József Library, Kecskemét, Hungary); it is descriptive work and does not aim to compare the data and results with those of other libraries.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".