Escape Outdoors: evaluating social media with Davey and Sky
Bibliographic record
Abstract
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 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".