Hacettepe, British Columbia ve Simon Frazer Üniversitelerinde Belge Yönetimi Uygulamalarının Karşılaştırmalı Analizi / Comperative Analysis of the Records Management Practices at Hacettepe, British Columbia and Simon Fraser Universities
Bibliographic record
Abstract
[Turkish abstract]Çalışmada Hacettepe Üniversitesi’nde belge yönetimi programlarının geliştirilmesi sürecinde kullanılmak üzere, Kanada ve Türkiye üniversitelerinde belge işlemlerine yönelik uygulamaların karşılaştırılması amaçlanmıştır. Türkiye’de üniversitelerde belge ve arşiv uygulamalarını yönelik idari koşullar, Hacettepe Üniversitesi Beytepe Yerleşkesi örneğinde değerlendirilmiştir. Uluslararası örnek olarak Kanada üniversite sistemi içerisinde yer alan British Columbia ve Simon Fraser Üniversitelerinde belge ve arşiv uygulamalarına ilişkin koşullar, Hacettepe Üniversiesi ile karşılaştırmak üzere incelenmiştir. [English abstract]Basic aim of this study is to compare the practices of the records processes in the universities between Canada and Turkey, which may then be used to develop records management programs for Hacettepe University. The administrative condition of the records management practices in Turkish universities has been evaluated on the illustrative example of Hacettepe University Beytepe Campus. Furthermore, as an international example, necessary information about records and archival practices has been gathered from the University of British Columbia and Simon Fraser University, in order to prepare a basis for comparison with that of the Hacettepe University.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".