Bibliographic record
Abstract
The main purpose of this study is to provide academic insight on the niche market, the board game café sector. Geographic investigative approaches were utilized to uncover operational/locational dynamics adopted by these retailers. Board game cafés are independent retailers who adopt a mixed-use business strategy. These establishments embody the characteristics of food, entertainment and retail point sale capabilities. The paper provides background understanding the changes of table top games throughout different periods in history. Contextual information is provided on table top gaming categories that define board games of the 21st century. The study then presents research within the Greater Toronto Area (GTA) on a sample of 16-board game café locations. Using researcher defined sets of variables, geographic methods were utilized in attempts to better understand the characteristics of the market. Huff Model analysis was conducted to determine the relative market influence and exposure of each café location. K-Means cluster analysis using researcher selected census data and Environics spending’s estimates were used to classify census tract neighbourhoods into similar groupings. Seven user defined cluster groups by census tract were resulted, explaining the household profile characteristics within the study area. Combining the results of Huff Model and K-Means analysis determined that a favoured cluster grouping (Millennials & Millennial bearing households) was adopted by a majority of board game café trade areas. Using this cluster grouping as baseline indicators, a multi-criteria decision analysis was conducted to determine market gaps within the study area. The analysis revealed two areas in Toronto which are assumed to have untapped potential for board game café establishments.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".