PROACTIVELY SEEKING NEW KNOWLEDGE IN SOCIAL ENCOUNTERS FOR TEACHING
Bibliographic record
Abstract
The Information Systems (IS) faculty has the duty of preparing future IS professionals with relevant skills. Thus, IS faculty need to stay updated with the new technology and trends in the Information Technology (IT) industry. To achieve this goal, we advance the approach of proactively seeking new knowledge in all social encounters. By exploiting the opportunities of these social interactions, IS faculty can obtain new knowledge and valuable insight for teaching. In this research, we demonstrated this approach with our practice. We also reported the discoveries of our preliminary case studies. This study makes the following potential contributions. First, this research showcases how to broaden IS faculty's knowledge horizon to keep up to date by actively exploiting social encounters. Second, the cases reported here can be adapted and used as teaching materials for classroom discussion in appropriate IS courses. Third, IS students will benefit from the industry insights, emerging technology trends, and career advice derived from these cases.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".