The connection between the geographical stratification of athletic club and the effectiveness of child football training.
Bibliographic record
Abstract
IntroductionThe production of new players is the motive of many football clubs mainly in Europe, which have set up classes for the identification and instruction of young players. They see the production of young footballers as a fruitful investment for two reasons: a) in a few years some of them will staff their men's teams and b) others will be profitably sold. They aim to incorporate particularly gifted young players into their force and to train them according to their own particular philosophy in order to achieve their maximum level of performance (Reilly, Williams, Nevill, Franks, 2000). The Rules for the Official Approval of Teams adhered to by football federations in member-countries of the European Football Federation (UEFA, 2012) also contribute to this.The continually increasing demand for footballers creates the need for the carefully planned instruction of players, especially the young (Kock, Krauspe, 1996). Whereas once it was possible for a talented athlete to distinguish himself in any sport in a relatively short time, now it takes many years of systematic, planned training (Avgerinos, 2007). According to Simon & Chase (1973) it requires at least 10 years' preparation to reach the highest level of performance in any sport. This ten-year golden rule, as it is called, has been applied to football by Helsen, Starkes & Hodges (1988) who seek to identify all of the factors which influence the development of young athletes, with the aim of producing footballers by the safest, least risky methods. According to Bloom (1985) the stimulation received by athletes from the environment in which they grew up, stemming in the main from the urge to train and the skills obtained, leads to differences which can still be observed in maturity. Over the last few decades researchers have been studying the effect that the size of a town has on the development of an athlete and his career. Relative studies have also been carried out by Curtis & Birch (1987) on Canadian ice-hockey players, Carlson (1988) on Swedish professional tennis players, Baker & Logan (2007) on young Canadian hockey players, Cote, MacDonald, Baker, Abernethy (2006) on professional athletes in different fields, Farser-Thomas, Cote, MacDonald, Baker, Abernethy (2006) on adolescent swimmers, Lidor, Cote, Arnon, Zeev, Cohen-Maoz (2010) on team-sportsmen, Bruner, MacDonald, Pickett, Cote (2011) on Swedish, Finnish, American and Canadian athletes. Most of the studies that have been carried out mainly refer to very large countries such as Canada and the United States, the comparison being made between cities with populations greater or lesser than 500,000 inhabitants. Data acquired was mainly from professional or high-level athletes. The conclusions showed that athletes who grew up in cities with fewer than 500,000 inhabitants had a greater chance of reaching a high level of achievement than those who either came from very small towns or very large ones. These areas offer more chances for playing games, easier access to athletic facilities, greater safety, a greater range of choice, more positive social support and better organization (Curtis & Birch, 1987; Cote et al., 2006; Baker & Logan, 2007; MacDonald, King, Cote, Abernathy 2009; Fraser-Thomas, Cote, MacDonald, 2010).Other researchers maintain that population size isn't on its own advantageous to the development of an athlete and that other parameters, concerned with the social and cultural environment should be taken into account. Such parameters are the cultural tradition, athletic system and geographical distribution of the population (Baker, Schoner, Cobley, Schimmwer, Nattie, 2009; Lidor et al., 2010; Bruner, Macdonald, Pickett, Cote, 2011).The geographic stratification of the Greek populationThe geographic stratification of the Greek population during the 20th C. underwent significant changes. According to the 1920 census 22.09% of the population lived in urban areas, 15. …
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".