Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The sport industry has become increasingly more complex with the expanse of digital technology such as fiber optic internet access, 5G wireless communication, and blockchain, just to name a few. These advancements have shifted the amount and variety of data produced and available for analysis by sport organizations. Yet, sport organization front offices remain well behind other industry segments (e.g., retail, communications) in regard to handling, processing, and analyzing the volume and variety of data to advance business objectives. In this brief, we introduce the notion of artificial intelligence (AI) to sport management. While AI, as a concept, has been discussed for more than 50 years, this article provides a definition and overview of its historical trajectory for sport managers. Concurrently, the article also identifies the value proposition for AI capability, notably the natural language processing across four customer-centered domains: 1) listening to the public narrative, 2) automating the sales process, 3) computerized consumer content, and 4) self-operating service. Integration challenges are also addressed for sport organizations as they seek to increase their digital competence, achieve competitive advantage through technical innovations, and ultimately become more efficient in a data-driven world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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 it