Briggs, Lorne oral history interview
Bibliographic record
Abstract
He grew up in New Westminster, and at 10 years old, he remembers exploring ships that were docked there. Briggs married at 17 years old, and at 20 years old he needed a job to support his young family. His brother encouraged him to go the longshore hall, which he did on December 22, 1961. Briggs also worked briefly at MacMillan Bloedel, drove a truck for a lumber company, and tried working as an electrician prior to longshore. Briggs reports that his family is one of the few families that have four generations working on the waterfront:. He retired in 2005 at the age of 64, and during his career he worked in New Westminster and Vancouver. In this interview he largely discusses how automation and mechanization changed the work of longshoring, the camaraderie and brotherhood amongst the longshoremen, and why he believes the stereotype of longshoremen being “all brawn and no brains” is inaccurate. He states that there is no other labour job like shoring and that “you had to work it to understand it”.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.177 | 0.026 |
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".