Comparison of effectiveness of advertising expenses during broadcasts of main hockey events
Bibliographic record
Abstract
Title: Comparison of effectiveness of advertising expenses during broadcasts of main hockey events Objectives: The main goal of this dissertation is to compare the amount of money invested into the commercial advertisement during sport broadcasts at the ČT sport channel with viewer ratings. The used metric is a coefficient computed as a ratio between the viewer rating of the particular broadcast and the corresponding advertisement cost, normalized using Cost per Thousand method. Another goal to find out how much TV viewers are able to associate a hockey event with the name of advertiser is by the questionnaire. The final goal is to evaluate the type of event which is the most convenient for the advertiser from the point of view of the advertising price, viewer ratings and memorizing the viewer. Methods: Analysis of the secondary data provided by Czech Television and internet portal Mediaguru. Emphasis on utilization of internal secondary data for the evaluation of the efficiency of advertising investments into the chosen hockey events. Four chosen hockey broadcasts are Winter Olympic Games 2014 in Soci 2014, World Championship 2015 in Czech republic, World Cup 2016 in Toronto and World Championship 2017 in Paris and Cologne. The effectiveness is evaluated by method Cost per Thousand which gives the...
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".