The need for affiliation: An examination of university students' loyalty toward MLB teams
Bibliographic record
Abstract
The Social Identity Theory can play an important role in identifying the fan base for Major League Baseball (MLB) teams. The theory predicts that as one identifies more with a specific group or team, the level of loyalty felt toward that group or team would also increase. The present study examines the need for university students to affiliate with a group and the impact that affiliation may have on the loyalty felt toward a MLB team. Two questionnaires were administered to a sample of in-state and out-of-state students at the undergraduate level from the University of Rhode Island (URI). The main purpose of the first questionnaire was to identify the MLB team of choice for the majority of the convenient sampled URI students (n = 209). The second questionnaire was administered (at a later date) to the same groups of students sampled for the first questionnaire. The second questionnaire's purpose was to (1) to collect general demographic information about each participant, (2) to compare in-state URI students' need for affiliation to out-of-state URI students' level of need for affiliation, and (3) to identify the level of sport fandom (loyalty) that each participant possesses for the given (favored) MLB team. Results indicated that in-state students possessed higher levels of loyalty toward the MLB team of choice, while out-of-state students had a higher need to affiliate with a group. Results also indicated that there was a positive association between in-state students' level of need to affiliate and their level of loyalty felt toward the MLB team of choice for the majority of the URI students in the convenience sample.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".