Technology Use and Information Preferences of Digitally Engaged American Quarter Horse Association Members
Bibliographic record
Abstract
The purpose of this study was to assess AQHA members' preferences for obtaining equine industry information via digital media and give AQHA more knowledge about its digitally engaged membership, as it relates to members' needs and improvements for an expansion of the organization's mobile application. The American Quarter Horse Association (AQHA) is the largest equine breed registry and member organization in the world. Survey research was used in this study. Both quantitative and qualitative data were collected from a 26-question instrument developed by the researcher. Approximately 100,000 instruments were distributed and 5,707 responses were complete and usable. The response rate was 5.7%; however, a follow-up instrument was distributed to allow for generalization. Results revealed the typical respondent to be a white female, who is 42.5 years old and a general membership holder with AQHA. Most respondents earned a high school education, with many obtaining at least one college degree. The typical respondent resides in Texas, has a total household income of $100,000 or more per year, and does not rely on involvement within the equine industry for income. Results also revealed the typical respondent owns a smart phone and accesses the Internet several times a day, from home, via broadband technology. The typical respondent accesses mobile applications and would access a new AQHA-sponsored application. In regard to digital media use, the typical respondent accesses a variety of sources for information and believes digital media is an educational tool; however, the typical respondent is neutral in their opinion of social media and its uses. When considering a potential new AQHA-sponsored mobile application, the typical respondent expects to see AQHA news provided within it and expects to pay for pedigree and records research. It is recommended that AQHA consider the findings from this study in developing a mobile application. The demographic information, as well as the digital media use and mobile application information will be useful in creating an application to be used by the organization's members. It is recommended that further research be conducted on equine enthusiasts' needs and preferences related to obtaining industry information via a variety of platforms.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".