Bibliographic record
Abstract
This thesis is focused on marketing of the National Hockey League (NHL) from 2005 to 2015. The NHL, having been founded in 1917, is the oldest and most famous ice hockey competition in the world. The aim of the study is to investigate the role and the impact of marketing and its techniques on the NHL. I start with 2005 because the then season was completely cancelled due to disagreements among players and owners of the clubs. It is considered to be a turning point in the history of the NHL since a salary cap has been introduced and clubs could spent limited amount of money on players. The main attention of the thesis is directed at single marketing activities of the league - television broadcasting, outdoor games, sponsors, All-Star Games, lockouts, the salary cap and revenues. Considerable space is devoted to comparison with the biggest competitors of the NHL on the North American sport market. The findings from the research show that management of the league uses marketing and its tools very extensively. Marketing helps the NHL to fulfil its plans, bring new fans and attract lucrative sponsors. The study concludes with a prediction what the future will bring to the league. An extension of the NHL into new markets in Las Vegas and Quebec is set to happen together with growing revenues.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".