An examination of the use of alternative dispute resolution processes in Canadian mergers & acquisitions practice
Bibliographic record
Abstract
Disputes can arise during commercial transactions no matter how well planned the business was or how comprehensively drafted the contract signed between the parties. Alternative dispute resolution (ADR) processes, substituting litigation, have become popular for helping parties resolve disputes amicably, among other advantages. The purpose of this study is to consider opinions of academic scholars on the use of ADR processes in commercial transactions. Particularly, the extent of use of these processes for resolving disputes arising from mergers and acquisitions (M&A) in Canada is tested using a qualitative research methodology with participants (M&A practitioners and ADR specialists) from different provinces across Canada. The study evaluates the use of mediation and arbitration – both being the major types of ADR processes – and reveals the near non-existent use of ADR processes in M&A transactions including the reasons thereof. Finally, this thesis suggests ways by which ADR can be better utilized for M&A disputes.
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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".