Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how the National Hockey League (NHL) Ottawa Senators’ Twitter activity influences (if at all) their followers’ activity online, examine whether followers’ Twitter activity level consequently influences their perception of and perceived inclusion within the online central fan group that surrounds the Senators, assess whether membership can potentially influence followers’ enrichment of social identity, and investigate if fans’ identity – and thereby commitment to the team – is impacted by their level of Twitter activity. Design/methodology/approach The study adopted a case study method focusing on the NHL’s Ottawa Senators, and collected data through a two-phase approach. In the first phase, a focus group was conducted to obtain breadth and depth, as well as to inform and refine the development of questions to be used in the second phase: survey method. In the second phase, a survey was employed to enrich the explanations and attain deeper and broader understanding regarding the research questions. Findings The results showed that time spent in reading and engaging in tweets are predictive of commitment. This study also showed that it is not strictly an individual’s Twitter activity in relation to the Senators (i.e. replying and retweeting) which dictates their membership within the fan group. Rather, it is a person’s activity level with other Senators community members which determines their membership in the team’s fan nation. Findings also suggest that both the evaluative and emotional components of forming a group are lacking within the Twitter communication. This only furthers the argument as to why the Senators need to engage in dialogue with their followers. Research limitations/implications This was a study of one team over several months of only one season and, therefore, does not take into account a more long-term-oriented strategy to help formulate social identity and team commitment. Originality/value The findings of the study informed us that the only predictor that reflected a significant impact on the construction of a Senators fans’ social identity was the number of minutes an individual spent reading the team’s tweets per day. However, the study also showed that the expansion of the network with other fans is a critical feature of increasing the fan group identity; thus, sport organization’s should play a role in helping to foster further engagement with others.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".