FACTORS ASSOCIATED WITH GROWTH CHANGES IN "GAZELLES"
Why this work is in the frame
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Bibliographic record
Abstract
The goal of this paper is to identify the factors that precede and may cause sudden changes in the pace of growth of high-growth SMEs or gazelles. A retrospective longitudinal case study of seven high growth SMEs that had undergone a total of 14 sudden shifts in growth reveals that a number of events caused the changes of pace. Some were triggered by the entrepreneur's decisions while others resulted from events beyond his/her control. Management's motivation for growth was an important element and this motivation changed over time, being influenced by both success and problems associated with actual growth. The success of growth strategies also appears to depend on the firm's proximity to its client base and its ability to obtain the information required for sound decision-making. Lastly, the availability of tangible and intangible resources was found essential in allowing the company to seize growth opportunities and proximity to the business milieu may help accessing these resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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