How can information providers connect information resources to entrepreneurs to spur innovation in economic development? Co‐sponsored by SIG‐III and SIG‐USE
Bibliographic record
Abstract
ABSTRACT Information and knowledge have been recognized as valuable resources for gaining competitive advantage, streamlining decision making and improving strategic management. In an increasingly competitive economic environment, the survival of organizations is heavily dependent on their recognition of information as an important strategic resource (Choo, ). Entrepreneurs, who have been regarded as the key drivers of innovation and have frequently been credited with boosting economic development (Timmons & Spinelli, 2009), find themselves in an increasingly disadvantaged position as a result of an inability to compete with the financial power of large companies. The benefits that entrepreneurs reap from having access to quality information resources have been documented in many studies, however, the accessibility and preferences of the information resources varies by context. The focus of the panel will be exploring how information specialists and institutions help entrepreneurs access and use information resources to support their business ventures. Panellists will shed light on different international perspectives drawn from research conducted on different types of groups/organizations. Discussion will be geared to illuminate existing approaches that have been taken to meet the information needs of entrepreneurs, with a view to propose future courses of action.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".