Selected Proceedings from the 2000 annual conference of the International Leadership Association, November 3-5, Toronto, Ontario Canada
Bibliographic record
Abstract
Leadership rightly is associated with the presence of followers, but the traits associated with leadership are desirable in non leaders as well. Indeed, one could argue that a good leader is one who develops such traits among his followers, making himself dispensable and even redundant. This argument is appealing in principle, but it also has grounds in the practical wisdom of business (as opposed to management) where the goal of every capitalist is to set up one source of cash flow after another, ever freeing him or herself to undertake new projects. Perhaps it shouldn't be surprising, then, if we discover that traits associated with leadership are also desirable in the founders of new firms. Fairly consistent findings in the literature on the funding of new technology-based ventures support this supposition. We have recently found that empirical works on leadership from the resourcefulness perspective may contribute to a better understanding of some key criteria used by venture capital firms to screen new ventures. The Decision Criteria of Venture Capitalists Most of the research on the venture capitalists' investment criteria have found that the investors' decision-making process is embedded in multi-staged activities during the pre- investment period. The deal originating stage, which is the first step, consists of the identification of potential investment prospects. Since most of the latter are too small to be easily identified, venture capitalists often have recourse to either personal or professional intermediaries. As venture capital firms generally specialize by geographic region, industry, size of investment, and stage of investment, the second stage, deal screening, consists of selecting out prospects which do not belong to the investors' areas of specialization. At the third stage, deal evaluation, each venture is assessed on multidimensional characteristics including financial and marketing projections. Investigation may be undertaken to insure the
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.401 | 0.145 |
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".