Bibliographic record
Abstract
Leadership and management practices are key factors inthe sustainability of rapid growth and high performance in small- tomedium-size businesses (SMEs).Most SMEs are unable to sustain suchgrowth.This article identifies five management practices that can aidSMEs in adapting to this growth.In order to identify these practices,interviews were conducted with 15 CEOs from Canadian high-growthentrepreneurial ventures, and literature discussing complexity sciences andself-organizing behavior was reviewed. These practices provide the necessary infrastructure for SMEs when theformal structures and systems already established are unable to support therapidly growing company.The five practices, which are part of the largerbehavior of self-organizing, are business logic, capture and share information,build relationships, manage organizational politics, and leadership style. Establishing a clear vision and being available for the employees--coreconcepts of business logic--allows CEOs to steer the direction of the companywhile still encouraging employees to be creative and share new ideas.W.L.Gore & Associates is one example of a company that has been able to usesuch practices to maintain fast growth and high performance for almost 50years. (SRD)
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".