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Record W4386715096 · doi:10.1108/tcj-02-2023-0026

Escape Outdoors: evaluating social media with Davey and Sky

2023· article· en· W4386715096 on OpenAlexaffabout
Sherry Finney, Megan Penney

Bibliographic record

VenueThe CASE Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsCape Breton University
Fundersnot available
KeywordsInfluencer marketingSocial mediaNova scotiaMarketingAnalyticsSocial marketingMarketing researchDigital marketingSocial media marketingBusinessSociologyMarketing managementPublic relationsComputer scienceRelationship marketingPolitical scienceData science

Abstract

fetched live from OpenAlex

Research methodology Information for this case was gained first-hand as the case authors are also the protagonists. Care was taken to ensure case material was presented in an unbiased and accurate manner. Case overview/synopsis Sherry Finney, co-manager and partner at Escape Outdoors (EO), North Sydney, Nova Scotia, has just about completed a social media campaign collaboration with Cape Breton outdoor influencers, Davey and Sky. This was the company’s first collaboration with social influencers, and EO had done it to increase their follower base, particularly on Instagram. Defining measures of success was the task now facing Finney and her Sales and Marketing Assistant, Megan Penney. The campaign costs were in the range of $500, and if EO were to do this campaign again, they needed to understand the pros and cons and if it was a success. The campaign would end in a few days, and before it was finalized, Finney and Penney had to decide what final metrics would be required for evaluation and, specifically, how the campaign would be evaluated. Complexity academic level This case is intended for courses in social media marketing, marketing management, marketing analytics, digital marketing or entrepreneurship. The typical user of this case will be an undergraduate or graduate business student who has completed an introductory marketing concepts course.

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 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.367
Teacher spread0.286 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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