Bibliographic record
Abstract
The Mississauga Music Walk of Fame (MMWF) acknowledges the work of musicians and music industry members who have spent a considerable amount of time in Mississauga. Founded in 2012 and located in Port Credit Memorial Park, The Walk is home to the City’s most accomplished music industry talents. Noticing the lack of engagement at The Walk, the team aims to bring its stars into the 21st century through the introduction of digital innovations. The Walk Experience Project captures the imagination of its visitors and invites them to learn more about the City’s artists and their outstanding work allowing the Mississauga Music Walk of Fame to unlock its true potential. By developing an immersive experience available for mobile devices, visitors of The Walk open themselves to a magical interaction with musical legends, immersing themselves in their legacy. Also, the content management system provides a means through which those who maintain and enter individuals into The Walk of Fame can manage and update inductee content seen by its visitors. The proposed innovations will use augmented reality experiences to promote cultural heritage, and leverage data analytics. Through the acquisition of interactive analytics, the Committee has the potential to pitch and scale the idea to Walks of Fame all over the world. Being the first of its kind, the Walk Experience Project has the capability to be a true staple of how digital innovation can foster and celebrate community.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.115 | 0.026 |
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".