Bibliographic record
Abstract
In the public sector regeneration and turnaround market in the last quarter of the last century, privatisation has been the most commonly prescribed drug. The biggest difficulty, however, in the way of effective application of this drug, as repeatedly reported by potential users, has been “dealing with excessive manpower”; from which, almost without exception, the public sector enterprises have been suffering in the entire third world. Consequently, more often than not, privatisation has remained a mere paper prescription (or a topic for non-conclusive seminars). The Berlin-Chemie AG, a German pharmaceutical company offers a very refreshing experience in this context. It is a story of regenerating a public sector enterprise from a parastatal of a communist country, to a market sauvy company From a domestic to an international company. In its regenerating years, it got corporatised, privatised. And it all happened under the direction of the same CEO.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".