Internationalization plan for Leon Chouinard & Sons
Bibliographic record
Abstract
There is a lot of competition in the wood products market of Canada. That is why the Canadian wood product manufacturers are looking for opportunities from other countries. Traditionally, the greatest export country has been the United States; however, the economic situation of the country is quite weak. Therefore, seeking other potential trade partners in other countries is topical. The United Kingdom is an interesting target, because the building regulations of the country changed recently and, they favor timber construction. \n \nThe research was made on behalf of a Canadian timber-frame company during the summer of 2011. The goal of the project was to find out if there is any demand for their products in the UK. Data was collected through an Internet-based market study and a bureau research. The questionnaire was sent to a comprehensive group of different companies working in the construction market of the UK. Also, information was gathered from experts and from the Internet. \n \nThe bureau research was a success. It provided a lot of useful information about internationalization and about the United Kingdom. However the market study failed because the response rate was too low. Alltogether, the project gave a lot of useful information to the Canadian timber-frame manufacturer, and it will be very useful in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.185 | 0.040 |
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".