Digitization of Bulgaria’s ethnographic archival heritage
Bibliographic record
Abstract
Текстът замислен като описание на плановете и усилията на екипа на АЕИФЕМ да модернизира, да приведе съответствие със съвременните технически и научни стандарти и да направи видимо наличното в него архивно наследство. Въпреки лошите условия на съхранение на архивните материали до момента, колекциите се радват на значителен интерес и внимание от страна на различни социални и професионални групи. Целта на представената дигитализация е да се даде възможност за световно разпространение на ценните визуални и наративни данни за културата, особено от прединдустриалния ѝ период, които досега не са били широко популяризирани. Библиография: Левски, Д. (2017). Цифрови светлинни сензорни матрици – от ерата на динозаврите до днес. Българска наука, 96. Първанов, И. (2021). Дигитален атлас на българския танцов фолклор. Зони на разпространение според теренните проучвания в етнохореологията. PhD dissertation, manuscript, Sofia: IEFSEM. Brennen, S., & Kreiss, D. (2014). Culture Digitally – Digitalization and Digitization. Retrieved from http://culturedigitally.org/2014/09/digitalization-and-digitization/ (accessed 05.03.2022). Butterworth, J., Pearson, A., Sutherland, P., & Farquhar, A. (2018). Remote Capture: Digitising Documentary Heritage in Challenging Locations. Cambridge, UK: Open Book Publishers. DIN–German national standard. (2019). DIN SPEC 15587 – Recommendations for digitization of cinematographic film. Retrieved from European Standards: https://www.en-standard.eu/din-spec-15587-recommendations-for-digitization-of-cinematographic-film/ (accessed 22.04.2023). Fulton, W. (1997-2019). A few scanning tips. Retrieved on November 03, 2019, from https://www.scantips.com/chap3c.html (accessed 30.04.2023). Iraci, J. (2017). The Digitization of VHS Video Tapes. Retrieved from Canadian Preservation Institute: https://publications.gc.ca/collections/collection_2018/pch/CH57-3-1-31-2016-eng.pdf (accessed 19.04.2023). Iraci, J., Hess, R., & Flak, K. (2017). The Digitization of Audio Tapes. Retrieved from Canadian Conservation Institute: The Digitization of Audio Tapes – Technical Bulletin 30 – Canada.ca (accessed 19.04.2023). ISO/TR 13028:2010. Retrieved from International Organization for Standardization: https://www.iso.org/standard/52391.html (accessed 20.04.2023). Johnson. (2021). Preservation Digitization Standards for the Digitization of Physical RNA records. Retrieved from National Archives of Australia: https://www.naa.gov.au/sites/default/files/2022-01/Preservation-Digitisation-Standards-2021.pdf (accessed 20.04.2023). Leggett, E. (2021). Digitization and Digital Archiving: A Practical Guide for Librarian. Rowman & Littlefield. Microsoft Docs. (2017). Types of Bitmaps. Retrieved on 10.11.2019 from https://docs.microsoft.com/en-us/dotnet/framework/winforms/advanced/types-of-bitmaps?view=netframework-4.7.2 (accessed 22.04.2023). Pushkar, O., & Sibilyev, K. (2011). Information Systems and Technologies. Summary of Lectures. Retrieved on October 12, 2019, from http://www.repository.hneu.edu.ua/handle/123456789/5879 (accessed 25.04.2023). Stark, H. (1982). Application of Optical Fourier Transforms. New York: Academic Press. Villarreal. (2020). The Use of 3D Apparel Simulation Software for Digitizing Historic. PhD dissertation, manuscript, NC State University Libraries. Retrieved from https://repository.lib.ncsu.edu/bitstream/handle/1840.20/38170/etd.pdf?sequence=1&isAllowed=y (accessed 22.04.2023). Zigouris, P. (2021). Contemporary Methods of Digitization. Athens: Panagiotis Zigouris.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".