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Record W2980106770 · doi:10.1177/2167479519878676

#WeTheNorth: Examining an Online Brand Community Through a Professional Sport Organization’s Hashtag Marketing Campaign

2019· article· en· W2980106770 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCommunication & Sport · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsLaurentian University
Fundersnot available
KeywordsBasketballSports marketingAdvertisingKey (lock)BusinessMarketingPublic relationsComputer sciencePolitical scienceMarketing managementRelationship marketingGeography

Abstract

fetched live from OpenAlex

The use of hashtags has become an important strategy in digital marketing, anchoring online conversations. The conversations stemming from the use of hashtags can comprise both meaningful dialogue between users and brands, and one-off spontaneous sentiments. As such, hashtags can aid in the formation of a team’s online brand community and can be useful to understand and target key segments of users. In this research, an examination of the Toronto Raptors’ #WeTheNorth campaign in the National Basketball Association was engendered to highlight (a) the types of communication networks formed through the use of the hashtag and (b) the types of segments that are derived from the hashtag (as well as their characteristics). The findings present insights that provide pertinent antecedents for future marketing activities for sport brands as they seek to develop communities of identified fans.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.092
GPT teacher head0.344
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it