Bibliographic record
Abstract
The MSC4 is the flagship of the Malaysian Social Science Association, held biennially since 1997. Its objectives are to bring together scholars from Malaysia and abroad to deliberate on the various dimensions of Malaysian studies, to draw comparisons between Malaysia and other countries, to strengthen networking and community building among social science scholars, and to initiate collaborative activities of mutual benefit such as research, publications, workshops, and others so as to advance Malaysian studies. This year’s MSC the fourth in the series is the biggest so far showcasing four keynote addresses, three special plenary sessions, 140 papers presented in 38 concurrent sessions, two book launches, and a book fair participated by four national and international publishers. It has also brought together about 200 participants – the majority from the various Malaysian universities, while about a quarter from abroad, namely scholars from the ASEAN region (Singapore, Indonesia, Thailand, and the Philippines), as well as from Japan, Australia, the United Kingdom, Germany, Finland, the United States, and Mexico. MSC4 is without doubt the most significant and the biggest Malaysian studies congress ever held in this region.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 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".