Bibliographic record
Abstract
An officer of Global Affairs Canada from 1990–2018, Geoff White is a career expert in Canadian foreign policy. In Working for Canada he shares that expertise, illuminating the often invisible work of creating and enacting international policy. Writing with clarity, wit, and common sense, White demystifies Canadian diplomacy and provides a clear view of how it actually works—and when it doesn’t. \n \nReflecting on the headlines, highlights, and sometimes scandals of a long and successful career, White offers a highly readable blend of personal recollection and political insight. He begins with his first assignment in communications planning during the 1991 Persian Gulf War and continues through the establishment of NAFTA, humanitarian intervention in Kosovo, softwood lumber, during assignments at headquarters and in Canadian embassies abroad. He shares his experiences of negotiating aviation agreements with foreign governments, and of diplomatic efforts aimed at restoring and protecting human rights. \n \nWorking for Canada is a fascinating memoir tracing a career spent in the service of Canada and Canadians. At the same time, it provides an unparalleled insider view into communications, negotiations, international trade, and diplomacy.
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 | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".