Bibliographic record
Abstract
ABSTRACT \n \nDamming the Remains: Traces of the Lost Seaway Communities \n \nRosemary O’Flaherty \n \nConcordia University, 2016 \n \nDamming the Remains examines the lived experience of 11,000 people who lost their homes in the 1950s to the St. Lawrence Seaway and Power Project. Ten villages on the river front were lost altogether in Eastern Ontario and northern New York State, while another five were negatively impacted. On the Canadian-side, these have become known collectively as the "Lost" and "Survivor" Villages respectively. \nThe flooding for the Power Project drastically altered the landscape along the river creating unsightly mud flats encouraging the growth of pestilential weeds that trapped debris from the river. Some points of land that would not be flooded, particularly on the American shore, had sections carved out and placed elsewhere in the river where a build-up of soil served to reduce the river’s flow rate to one more suitable for the generation of electricity. As a result, along with dredging for a deeper shipping channel, both shallow and deep areas of the river were transformed affecting previous habitats for flora and fauna. \nBased on extensive archival and newspaper research, this thesis includes oral history interviews with former residents and incorporates visual evidence with considerable participant observation as well as on-the-ground exploration of the physical remnants. This thesis builds upon the work of Daniel Macfarlane, Joy Parr, and others in exploring how the memories and perspectives of the dislocated residents have evolved over the past fifty-eight years. It was particularly useful to compare the oral interviews conducted by the Lost Villages Historical Society in the late 1970s with those I obtained between 2008 and 2016. Of particular interest is the study of how local people individually and collectively remember the inundation and the resulting losses. As a cross-national study, this thesis enhances the sparse scholarship in the United States on the rearrangement of the land and river that displaced 1,100 people in New York.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".