Bibliographic record
Abstract
Claiming oppression is easy. Only the low bar of unfairness must be overcome. It seems to arise from any unwelcome conduct in a (usually) closely held corporation. It can be appended to any corporate misconduct claim. Broad statutory language governs the remedy, making it facially applicable to a broad range of conduct. In addition, the remedy is fact-based, being granted when a party satisfies the court that the corporation or its directors acted in a way that is oppressive or unfairly prejudicial to, or that unfairly disregards the interests of, any security holder, creditor, director or officer. In the face of these challenges, courts have struggled to maintain a clear set of applicable rules to govern when oppression has occurred. As a consequence, predicting the outcome of an oppression case is difficult. This article prescribes how courts can achieve greater clarity in cases where a party has alleged oppression by developing a principled approach to determine whether the impugned conduct rises to the level of harm required by the statute. This approach has two parts. The first part identifies the elements necessary to entitle the applicant to an oppression remedy and combines them to form two overarching principles. The second part of the approach discusses the effect of the impugned conduct on a complainant to show how prejudicial conduct or conduct that disregards the complainant can become conduct that is “unfairly prejudicial” or that “unfairly disregards” the complainant.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".