PRIVATE EQUITY INVESTING IN EMERGING MARKETS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
After a proliferation of emerging market funds in the 1990s, growth has slowed drastically due to disappointing preliminary results. Private sector funds initially appeared promising because of the burgeoning demand for capital in emerging markets, the new receptivity of governments to foreign investors, and the prospect of high returns. But in many cases, the regulatory and legal frameworks did not provide adequate investor protection, and dramatic differences in accounting standards, corporate governance, and exit potential created problems. These problems are often accentuated because local owners are adept at navigating the legal and accounting systems, placing investors at a disadvantage. As global competition intensifies, local policies, regulations, and business practices are becoming increasingly important in attracting investors. Local governments must institute the reforms necessary to improve the investment environment, including the strengthening of shareholder rights and corporate governance standards and improving access to public equity markets. Development finance institutions must provide direction and leadership in these areas. And fund managers must align their business models more closely with emerging market realities by establishing a local presence, adopting a more hands‐on approach to monitoring their investments, and developing creative exit strategies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it