Moose Jaw's "Great Escape": Constructing Tunnels, Deconstructing Heritage, Marketing Places
Bibliographic record
Abstract
The challenges of a post-industrial world are prompting new initiatives in marketing heritage as constructed mythologies, popular entertainment, tourism, and economic development. A combination of nostalgia for an imagined past, economic and cultural insecurity, and a growing demand for the consumption of entertainment has made a multifaceted engagement with the past the stuff of economic policy. What's going on? Is it a rear-window nostalgic gaze as our lives and places lose their distinctiveness in globalized morphings into a predictable sameness? Is it place marketing in placeless times as a late-industrial economic strategy? Clearly, heritage formation is a dynamic process and the very successful marketing of the Moose Jaw tunnels provides us with an excellent demonstration of the process. Resume Les defis souleves par un monde postindustriel incitent a lancer des initiatives visant a commercialiser le patrimoine en tant que mythologies edifiees, divertissements populaires, tourisme et developpement economique. Un melange de nostalgie pour un passe imagine, une insecurite culturelle et economique, et une demande croissante pour la consommation de divertissements ont fait d'un engagement aux multiples facettes avec le passe l'objet d'une politique economique. Que se passe-t-il ? S'agit-il d'un regard nostalgique sur le passe au fur et a mesure que nos vies et les endroits ou nous vivons perdent leur caractere distinctif dans des morphages mondialises d'une monotonie previsible ? S'agit-il d'une « commercialisation des lieux en des temps qui n'ont pas de lieux » en guise de strategie economique de la fin de l'ere industrielle ?De toute evidence, la formation du patrimoine est un processus dynamique, et la commercialisation tres reussie des tunnels de Moose Jaw en constitue un parfait exemple.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".