Bibliographic record
Abstract
The difficulty large, diversified firms have in generating and sustaining innovation has long been recognizes. Excessive hierarchies, lengthy decision making, and oppressive procedure manuals and controls are just some of the all-too-common features typically and frequently cited as being found in these organizations. As a remedy, the conventional wisdom of recent times has been captured in the three's: de-bureaucratize, de-layer, and decentralize. In the following passages, I will take you through several examples of how bureaucracy and tight controls are facilitating, contributing to, and supporting the innovation efforts in some large, diversifies, and well-known corporations. In some instances, bureaucracy us masquerading under some faddish, rehabilitated, or politically correct label- a sort of flavour-of-the-month management wisdom. It is bureaucracy nonetheless. The result is surprisingly tough, disciplined, almost martial arts approach to innovation.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".