Bibliographic record
Abstract
During the nineteenth and twentieth centuries, small towns in Canada developed around a Main Street. This is where the shops, the restaurants, the hotels and the important public buildings were built. Yet, following the end of the Second World War, Canada’s small historic towns and their Main Streets began to deteriorate. Various initiatives were undertaken to revitalise them, notably the urban renewal efforts of the 1960s and the beautification schemes of the 1970s. For the most part, these efforts failed. In 1979, the Heritage Canada Foundation launched a programme called Main Street Canada and developed it on the premises that downtowns are complex entities: to prosper they must develop both economically and environmentally. The programme's strategy was based loosely upon the approach that the rival shopping centres used: open an office in the heart of the shopping district; install a coordinator who lives and works in the community; operate a programme based on the four-point approach of organisation, marketing, economic development and design improvement. This approach was incremental, emphasised widespread local participation, developed local resources and promoted local communal identity. This approach was successfully implemented in hundreds of Canadian small communities and in thousands in the USA.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".