"Continuous Response Evaluation of Digital Clips Over the Internet"
Bibliographic record
Abstract
I have great privilege to attend and co-present our research paper entitled “Continuous Response Evaluation of Digital Clips over the Internet” with Dr. Griff Richards at the EDULEARN09 Conference, Barcelona, Spain between July 6 and July 8, 2009. The Edulearn09 Conference has counted with 340 oral presentations and 80 posters. We have presented our paper on July 7, 2009. \n \nThe paper is well received by the audiences. They are interested in our innovative continuous response system. They would like to know if AU can make the system and technologies available for their project use. Overall, The audiences have provided favorable response to our presentation and are impressed with our E-learning project. We see this conference as a good opportunity for us to raise AU research profile and showcase our excellence in E-learning research and scholarship. This multidisciplinary and multicultural experience also allows us to share ideas and learn from each other with people coming from more than 65 countries. The paper has been published in the conference proceedings.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".