Bibliographic record
Abstract
What kind of self images characterise a football supporter? This question is the main focus of this thesis. Therefore, the thesis is trying to find distinguishing characteristic traits for football supporters. This work has been carried out through qualitative interviews with individuals who define themselves as football supporters. The interviews were mainly focused on two different types of supporters, official supporters and other supporters. The official supporters consist of individuals from official supporter clubs which are officially linked to a specific team in the first division in Sweden, the Swedish Allsvenska. The other supporters consist of individuals who are also supporters but they express their supportership in a different way. The interviews were analyzed using the Canadian sociologist Erving Goffman’s theories of dramatism, specifically from the book, The Presentation of Self in Everyday Life (1959). The thesis concludes that the phenomena of supporters is affected by a widely spread heterogeneity and has been exposed for a major differentiation, which has led to the design of many different types of supporters. Some of these different types of supporters are noticed in the thesis, such as Official supporters, Low commitment supporters, Lower division supporters and TV-supporters. The thesis also casts light on, that more types of supporters exist, for example Hooligans, Casuals, Ultras, Supporters of success etc. The analysis of the interviews concludes that different roles are acted out in the performances of a supporter. All the different types of supporters have different types of role-descriptions (actions, behaviors and traits) which are characteristic for their type of supportership. Therefore, all supporters can be seen as if they consist of two different types of characteristic role-descriptions as a supporter. These characteristic roles are; the general supporter and the specific supporter. The general supporter role is characterized by different types of actions and traits which are general for all supporters. The specific supporter role is characterized by specific actions and traits commonly connected to a specific type of supporter. The thesis also concludes that the supporters are well aware of the on-going heterogeneity and therefore, all supporters can be seen as smaller parts in the whole picture of supportership.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".