Bibliographic record
Abstract
since december 1963 when I purchased my first Liberal Party membership, I have participated in the whole gamut of party activities.I've stuffed envelopes, ran as a candidate (four times), raised money, and served party organizations right up to the National Executive of the Liberal Party of Canada-all the while living and earning my living in Alberta, not exactly a Liberal Party breeding ground.Despite this hostile political environment, through it all, I learned much about Canada and the provinces, observed some history close up, and met many interesting and powerful people.Sometime after I ran in what was to be my last campaign in 1984, I decided that I should share some of my experiences in Canadian politics and history, and so I set about writing about them.This narrative describes events of the years 1968 to 1972, the first term of the Trudeau government and an era of great political change in Canada and Alberta.It is written from my perspective as an Alberta Liberal Party activist who at that time hovered only around the edges of political power, but who nevertheless acquired a pretty good knowledge of his province and his party.Notwithstanding my party
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.396 | 0.229 |
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".