Canadian e-Business Initiative (CeBI) (www.cebi.ca)
Bibliographic record
Abstract
Canadian based holding company that owns and oversees some $8.2 billion Cdn. of assets and investments in several industries including: telecommunications, energy and water utilities, forest products, oil refining, fertilizer production, food-processing, mining, e-commerce, venture capital and biotechnology. As President and CEO, Mr. Hart is responsible for governance and oversight of some fourteen wholly owned or majority controlled corporations, as well as numerous other minority ownership investments: Since joining CIC as its President and CEO, regular shareholder dividends have been significantly increased with cash dividends averaging in excess of 12% per year over the past several years. At the same time total enterprise debt has been substantially reduced. During Mr. Hart's tenure as CEO, CIC has been recognized by the Conference Board of Canada for Canadian leadership in corporate governance practices. Prior to joining CIC as President, Mr. Hart was a Vice President with the international accounting and consultancy of KPMG working alternatively in the firm's corporate strategy & finance practice as well as in its public sector industry practice. Mr. Hart also served for two and one half years as Deputy Minister, Economic Development and Trade for the Province of Saskatchewan and was involved in leading numerous trade missions and negotiations concerning trade agreements such as NAFTA and AIT. Mr. Hart has served, or currently serves, as a corporate director with a number of business corporations and organizations. He has served in several volunteer roles both in his community and at the national level in Canada including the
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.428 | 0.170 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".