Bibliographic record
Abstract
This is part of a memoir that was not intended as a neurological journal contribution but it has so evolved.My theme, if there has truly been one, was to point out to those who would aspire to a long medical career, if lucky, energetic and dedicated, they may look forward to a life filled with interest, not the least of which are encounters with both patients and discoveries of great interest, but also of remarkable people, some in a great variety of strange places.Some who are featured were encountered during professional commitments, others with family.For convenience they will be dealt with geographically rather than temporally.I will begin this part by discussing personalities and discoveries from Canada, plainly best known to me.Joey Smallwood led Newfoundland and Labrador away from its 400 centuries of British colonial exploitation to become Canada's tenth province (almost 100 years after the others had joined together).He was a man who wished to present himself as a "peoples' person".In 1964 Kay and our four children went on a camping trip that took us across the width of our new province on the barely completed transcontinental highway from Cornerbrook to St. John's.We spotted the famous man entering the government office building: "How would you kids like to meet a famous Canadian?"His secretary was somewhat taken aback with the request but he came out from his "Oval Office" to greet effusively six moderately dowdy campers, one (Ian) still smeared here and there with the lime dust provided for use in the nearby campsite's outdoor privy.To our delight the friendly Joey, shook hands all around and then apologizing that he had a busy schedule, went back inside.If charisma trumps other qualities in a politician he had his share.Smallwood decided that a country consisting of scores of fishing outports scattered along its lengthy and tortuous coastline connected to each other only by ships, could never provide reasonable educational or medical care facilities.With his usual energy and initiative he set out a plan to consolidate the population, discouraging the persistence of tiny but truly isolated communities.In this 1964 camping excursion we were witness to whole houses being transported on barges pulled by tug-boats in the Straits of Belle Isle as part of this resettlement.In 1965 I served part of a summer as a volunteer physician on the Grenfell Outport Hospital Ship, based at St. Anthony at the tip of the Northern Peninsula.We weathered rough seas in storms, and in fog watched for icebergs.We were several days fog-bound in Red Bay, Labrador.One evening the fog abruptly lifted: "Hey, Doc, how would you like to bring your son and we will go to Black Duck (population=2).It's 20 miles and the sun does not set until 11.The old man there has a sealing gun you might buy."
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.964 | 0.944 |
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".