Bibliographic record
Abstract
Summerside's modern support of Arts and Heritage dates back 45 years to modest beginnings. Summerside is no stranger to community resilience. Summerside bounced back from a shipbuilding centre to become an Island transportation terminus, followed by a world leading silver black fox production and export centre and finally the home of a military base for 50 years. 1989 was a pivotal year - the Canadian Forces Base Summerside would be no more. The citizens of this town spoke as one fighting to retain it, without success. A Federal government Tax Centre would be its replacement, itself an extraordinary feat as not every former military town would receive that level of investment by the Federal government. Cultural initiatives helped sustain the community. Summerside Bounces Back is a panel presentation on the role of resilience told by four different cultural players. Two are very successful community based non-government cultural initiatives and two are equally successful government (one provincial, one municipal) initiatives that enjoy a high level of community connectedness. You will feel you know Summerside when you meet the players who will share why and how community resilience played a part in their successful establishment. Which came first, the resilience or the culture?
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.023 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".