Where Do We Go From Here? A Quantitative Analysis of Alberta's Foreign Office Network
Bibliographic record
Abstract
The raison d’être for Alberta’s foreign office network is to find new opportunities and build partnerships internationally. The Government of Alberta, like other provinces, maintains foreign offices as part of a set of strategies to help firms navigate the intricate international market, promote Alberta industry capabilities and expertise to potential investors, proved timely policy and trade information back to the Ministry of International and Intergovernmental Relations, and endorse Alberta as a world-leader in environmental standards globally. The joint effort of these strategies suggest that these offices are aimed at fostering sustainable economic growth for the province, so how well is the government doing at meeting that goal? Alberta’s new NDP government has briefly made reference to a commitment to diversifying and expanding the Alberta economy, but has not yet revealed their foreign strategy or a mandate direction for the Ministry of IIR, so there is an opportunity to review Alberta 45 year paradiplomatic strategy. At present, the Government of Alberta supports eleven foreign offices in eight countries under the Ministry of IIR with an annual budget of $10.932 million annually. Alberta currently maintains international office: Alberta China Office; Alberta Shanghai Office; Alberta Hong Kong Office; Alberta Taiwan Office; Alberta Japan Office; Alberta Korea Office; Alberta Singapore Office; Alberta India Office; Alberta United Kingdom Office; Alberta Mexico Office; and Alberta Washington Office. Alberta tax payers are footing the bill, and in an ideal world are receiving some (economic) benefit from them. The million dollar question then is do these offices actually create benefits and greater exports for the province?
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".