Organisation theory and sport management
Bibliographic record
Abstract
OVERVIEW Why would you want to know about organisation theory? Well, look around you. We live in a world that is full of organisations of different sizes, types, and goals. Sport organisations, of course, are no exception. Most of you will likely work in some type of organisation(s) now or in the future, notwithstanding the fact that the university or college you now attend is also a type of organisation. But why should a sport manager be concerned with organisation theory?The analogy of a car is useful here. Many of us know how to drive a car, but relatively few of us know what to do when it breaks down! So what do we do? We lift the hood and look at the motor. Again, relatively few of us know what we're looking at. We might take a stab at a quick fix, but are never really sure if we've sorted out the problem. Sometimes we might even ignore the problem, hoping it will go away. More often than not, this leads to a worsening of the situation. Eventually, the car breaks down altogether and is rendered useless and in need of costly servicing or, worse, total destruction. Now think of a sport organisation. What is the manager's role when something goes wrong? Of course, s/he is expected to know how to solve organisational problems as and when they arise. But how many sport managers know exactly what to do when they 'lift the hood' on their organisation? A basic grounding in organisation theory arms the sport manager with this knowledge, and helps us to recognise the symptoms of potential organisational problems before they actually arise, thereby keeping our sport organisations on the road' and running efficiently.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".