Bibliographic record
Abstract
Enron offers a prime case study to explore the many failures of the modern corpora-tion to treat its employees even-handedly, ethically and with respect. Employees make significant personal investments into the corporations they work for, but employee interests are rarely of concern to directors. This paper analyses several theories on the nature and purpose of the corporation and argues that the interests of stakeholders other than shareholders ought to be legally recognised in the corporation. The paper examines three theories on employees and employment, and argues that the human capital theory is most meaningful and best reflects the role of the employee when that employee is analysed in a broad social, political and economic context. The authors evaluate reforms, such as stakeholder statutes, which have been implemented in various jurisdictions, and set out specific amendments that ought to be implemented to recognise the status of the employee as an important corporate stakeholder in the modern corporation. Tuvia Borok is a graduating law student of Osgoode Hall Law School in Toronto, Canada. This autumn, Tuvia will pursue his master’s of law abroad focusing on the study of global corporate markets. He is scheduled to return to the firm of Osler, Hoskin & Harcourt in Toronto, Canada, after his graduate studies.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".