Collaboratively Building Digital Libraries: Focus on Local Historical Resources for Educational Use
Bibliographic record
Abstract
COREDEV (Collaborative Resource Development) is a proposed digital library for historical resources that supports the development of digital content collaboratively. A prototype biographical portal that could handle information on Malaysian personalities was chosen as the domain for the test-bed. The biographical portal incorporates five main basic features: (a) uploading, indexing, searching and retrieval modules supports the creation, capturing and sharing of historical data from distributed sites and user groups (This environment helps produce the desired outcome in terms of ICT literate teachers and students and provide the experience of creating or publishing in digital libraries); (b) supporting multi-format digital resources (text, images, audio and video clips); (c) providing a facility for searching the contents of the digital libraries from simple keyword searches, specific occurrences of words in specific fields and a combination of terms using Boolean operators; (d) providing user controlled display (user may choose search and retrieval screens either in Malay or English language, determine the number of results to be displayed (5, 10 or 20 records and browse thumbnail objects before zooming on specific details); (e) ensuring basic security features (authentication, registration of users and requirements of validation for all uploads by members before it is searchable through the Internet). Other information provided includes a brief introduction about the system, frequently asked questions (FAQ), terms and conditions for those interested in participating, help and edutainment features and linkages to other related resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".