Bibliographic record
Abstract
Founded in 2014, SuperMonkey is an innovative gym brand that has disrupted the traditional gym membership model with its "pay-per-session, no annual memberships; professional coaches, no sales pitches" approach. Initially, SuperMonkey offered a unique fitness experience through shipping container gym pods before shifting its focus to group classes. SuperMonkey’s gyms are lively group class spaces where clients can follow professional instructors, immersed in dynamic lighting and music. This transforms what could be a solitary and monotonous workout into an energetic and social event. By the end of 2022, SuperMonkey had attracted over 500,000 paying users and had opened or was planning nearly 200 stores in first-tier and emerging cities. However, as market competition intensifies, SuperMonkey faces new challenges, particularly regarding its pace of expansion. HILEFIT, a competitor established around the same time, had expanded to 1,300 locations by 2023. SuperMonkey must now carefully consider whether to maintain the quality of its fitness services to ensure customer satisfaction or accelerate the opening of new stores to capture a larger market share. This dilemma poses a significant test of their strategic decision-making skills.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.468 | 0.195 |
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".