Breakdown: The Inside Story of the Rise and Fall of Heenan Blaikie by Norman Bacal
Bibliographic record
Abstract
The Canadian legal landscape is changing. Data over the last three decades show a trend toward larger law firms. Many of the country’s most storied ‘big law’ corporate firms have exploded in size and reach. Almost all of these firms maintain offices across the country and satellite offices in key international markets. Other large firms have been subsumed into foreign conglomerates pursuing expansion into the Canadian legal market. These developments have led to an increase in revenues and business opportunities for senior partners at these firms. It has also led to unprecedented challenges for the management of big law firms in Canada. As one of the former managing partners of Heenan Blaikie LLP (Heenan Blaikie), Norman Bacal knows this better than most.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.051 | 0.015 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".