Spordi otsereportaaž Vancouveri taliolümpiamängudel ETV, NBC, CTV, SVT, YLE, ZDF ja Eurospordi näitel
Bibliographic record
Abstract
Sports Live Broadcast on the Vancouver Winter Olympics based on the coverage of \nETV, NBC, CTV, SVT, YLE, ZDF and Eurosport. \nThe main purpose of this bachelor’s thesis was to examine the genre of television live sports \nbroadcast. The comparison was based on the 2010 Vancouver Winter Olympics broadcasts on \nETV, NBC, CTV, Eurosport, SVT, YLE and ZDF. Focus was on the sportscasting and the \nroles of reporter and expert. Both quantitative and qualitative content analysis was used when \ncomparing recordings of three different types of Winter Olympic events – ice hockey, crosscountry \nskiing and figure skating. \nIt was discovered that diversity between broadcasting different events were more significant \nthan diversity between broadcasters. The main disciminating factor within the sport itself \nwas its pace. For example, figure skating is a lower pace sport and because of this the broadcast \nturned out to be more conversational and there was less description of live action. As a \nresult, expert got more time in the broadcast. Ice hockey, to the contrary, has a higher pace, so \nmost of the time went into describing immediate actions leaving less time for expert’s commentaries. \nReporters’ and experts’ work was also influenced by the agreed share of their roles. It \nturned out that the North-American broadcasts had thoroughly defined the role of expert and \nthis meant that experts were given more time to be involved in the broadcast. Contrary to this, \nin European and especially in Estonian broadcasts the reporters often abandoned the role of \nmediator and took over many traditional roles of expert. This meant that often reporter’s so \ncalled expert opinion had doubtful value and experts themselves had a lesser role in the \nbroadcast. \nAccording to the former Estonian sportscaster Toomas Uba (1968) the most important \nrole of the reporter is to be the mediator of the emotional tension curve in the sports broadcast. \nIt turned out that reporters have basically two methods to differentiate critical and less \nintensive moments: first, faster and more intensive way of speaking versus calmer conversational \nstyle, and second, more description of live action versus more background information. \nEstonian Television broadcasts differed from other broadcasts mostly in the cooperation \nof commentators. The main factor in this category for Estonian Television was the sound delay \nand distance between reporter and expert. Both were caused by the fact that reporter was \ncommenting on spot in Vancouver and expert had to comment at the same time in Estonia. \nFrom all the broadcasters analyzed only Estonian Television had this problem. It can be said \nthat this sort of different positioning of the commentators is a cheaper way of broadcasting, \nbut it sets unnecessary limits to commenting and hampers its quality. \nAnother negative aspect of Estonian Television broadcasts was the fact that all experts had \nvery little experience in sportscasting. This meant that the experts were bad talkers and the cooperation \nof the commentators was rugged. \nTo conclude, one may say that the main flaws of Estonian sports broadcasts are related to \nweak cooperation between commentators, poor training of experts and vagueness of experts’ \nrole. At the same time, Estonian sports reporters do not differ much from sports reporters of \nthe rest of the world – the difference between sports events is much more significant than the \ndifference between reporters.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".