Marketingová strategie studentských průkazů ISIC v ČR a Kanadě
Bibliographic record
Abstract
Práce se zabývá světem průkazů ISIC a jejich fungování ve dvou různých státech. Autor využívá svých zkušeností ze svého působení na českém a kanadském trhu, a proto přináší na problematiku unikátní pohled, podpořený svou zkušeností. V praktické části pak předkládá návrh strategie pro zavedení průkazů ISIC na kanadském trhu. Čtenáři tak předkládá ucelený prohled na strategii dvou odlišných společností, které však vydávají stejný produkt, který je součástí života čtvrt milionu studentů v ČR. The final thesis is focusing on marketing strategy of ISIC cards in two different countries. Author is using his experience that he gained as Project Manager for ISIC Czech Republic and also ISIC Canada. In the final part it is proposing the strategy for ISIC within high schools in Canada. Therefore the thesis is showing unique view on the strategy of two different companies selling the same product.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".