Sherman Zavitz fonds, 1991-2021, n.d.
Bibliographic record
Abstract
Sherman Zavitz was originally from Welland but moved to Niagara Falls in 1960. He was a teacher for 35 years and taught at several elementary schools in the city during his career. After moving to Niagara Falls he became interested in the City’s history and learned about it by reading, talking to others, and exploring the city. In 1994 the Lundy’s Lane Historical Society nominated Zavitz for the position of Official Historian for the City of Niagara Falls, which was approved by city council. He remained in this role until he retired in 2019. He was also Official Historian of the Niagara Parks Commission. Zavitz wrote many columns for the Niagara Falls Review on the history of Niagara Falls, including two regular series, “A Niagara Note” and a photo feature known as “Niagara Then and Now”. In addition to these articles, Zavitz wrote several books on Niagara’s history including Niagara Falls: Historical Notes, Then and Now: Niagara Falls, Ontario, and It Happened at Niagara. Some of his other activities included hosting local walking tours, appearances on local TV and radio, past president of the Lundy’s Lane Historical Society, and serving on the Board of Directors for the Canadian Canal Society, the Friends of Fort George, and Niagara Falls Museums.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.224 | 0.082 |
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".