IMR 665: Management of Audio Visual Records & Archives. Individual assignment: Article summary / Nurarif Hilmi Muse
Bibliographic record
Abstract
Article on: Article 1 - “Clear skies or cloudy forecast?Legal challenges in the management and acquisition of audiovisual materials in the cloud” Article 2 - “Audiovisual materials in UK public libraries : economic sense?” The first article is about legal challenges in management and acquisition of audiovisual materials in the cloud. The author is Elaine Goh from University of British Columbia, Vancouver, Canada and published in year 2014. This article is mostly about legal challenges faces in term of management and questions about the control, ownership and custody of audiovisual materials in the cloud. Court cases that relate to the audiovisual materials is from three Commonwealth countries which represent North America, the Pacific and Asia. Furthermore from this article the court cases being analyze and author find useful principle the the archival field could draw to diminish some of those risks. Meanwhile for the second article is about audiovisual materials in UK public libraries in term of economic. There is three author for this article which is Anne Morris and Catherine Ayre from Loughborough University, UK and Amy Jones from Wellington Library, Telford and Wrekin Council in UK and published in year 2006. This second article is based on provision of audiovisual materials in UK public libraries and the economic value. The provision for audiovisual material in UK is quite wide and varied. The income from the public libraries is generated from the loans of audiovisual collection to support the services. A survey is taken from all public libraries in the UK to investigate current and future provision for the material.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.199 | 0.124 |
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".