MULTI-SCREEN VIDEO COMMUNICATION FOR BUSINESS AND ECONOMICS: LESSONS FOR MBA SCHOOLS OF THE 21ST CENTURY
Bibliographic record
Abstract
Responding to a global trend to extend methods of communication and teaching, considerable attentions have been paid in industries of various types as well as in education on the use of multiscreen video communication methods. Yet, in spite of its great potentials, cautions and negativism on extending conventional teaching platform to multi-dimensional levels persists at various levels, ( Green, 2010, Green & Wagner, 2011, Edmundson, 2012). This paper reports experiments in several classroom settings of Business and Economics courses conducted in the summer of 2012. The main conclusion of the study is that the need to use the extended platform is heavily activity dependent. Indeed, MBA schools aiming to embrace multi-screen video communication technology will be unwise to adopt a one-size-fits-all solution. Parallel development also has the advantages of offering easier matching of platform with activities, enabling gradual adoption and possibly a more effective way to manage obsolescence crucial in technology management of an organization.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".