Bibliographic record
Abstract
Sino-Forest, formed in 1994, was a "stock market darling which promised investors a way to cash in on China's rocketing economic growth by way of a booming domestic forestry business". 1,2The company claimed a market value of over $6 billion dollars and was Canada's largest, publicly-traded forest company.Sino-Forest's billion dollar success came to an abrupt end in June, 2011 when a short-seller investment firm specializing in Asia claimed that the company was a multi-billion dollar Ponzi scheme. 1 Following the allegations, Sino-Forests' stock price and bonds collapsed.As a result, Sino-Forest was forced to file for bankruptcy protection, which was granted.The company was dissolved and taken over by creditors as no buyer could be found.Several of the principles have since been charged with civil securities fraud by the Ontario Securities Commission.This paper examines the intricacies of the Sino-Forest scandal while contributing to a theoretical discussion on the role of structural holes in global capital markets.Here, we invoke Quinney's 3 analysis of capital, Chamblis's 4 notion of structural contradictions, and David Harvey's 5 theory of accumulation by dispossession ( 2004).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".