Analysis of Sport Marketing Researchers in Google Scholar
Bibliographic record
Abstract
Purpose: The purpose of this study was to analyze the researchers’ situations in the field of sport marketing based on data available in Google Scholar.Methods: This research was a descriptive and quantitative content analysis. The study population was all 229 researchers in Google Scholar who introduced themselves on the subject of Sport Marketing studies in March 2021. The data collection tool was a coding sheet and its instruction which was used after confirming its validity and reliability. The collected data was analyzed by descriptive statistics.Results: Findings showed from among 229 sport marketing researchers in the Google Scholar database, 84 were (about 37%) from the United States. After the United States, Iran ranks second with 60 people (about 26% of the research population). This frequency is significantly reduced in other countries. For example, Canada with 9 people, Japan and South Korea with 6 people, Greece with 5 people, Australia, Portugal and Turkey with 4 people, France, Spain and Taiwan with 3 people are in the next ranks. The other countries are in the next category with 2 or 1 representatives.Conclusion: It is noteworthy that only 37 countries had a research representative called Sport Marketing in the Google Scholar database.
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.
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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.074 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".