Business Development Support and Knowledge-Based Businesses
Bibliographic record
Abstract
The experiences of small- and medium-sized firms withbusiness development support in Newfoundland and Labrador, Canada, areanalyzed, with particular attention to the support needs of knowledge-basedbusinesses (KBBs) compared to more traditional firms. Data were obtained from a sample of 259 entrepreneurs in knowledge-basedbusinesses and traditional firms. Findings suggest that sources of support usedby KBBs do not differ from traditional firms. Significant differences exist inproduct development, market access, and training, where KBBs are more likely toutilize external support. Additionally, differences exist in the types offinancial support related to research and development, project financing, andexport financing. KBBs are more likely to rely on networks consisting ofindustry experts, professionals, and consultants. KBBs also have a higher levelof research and development activity, a strong export orientation, and highlyskilled workers. Results show that, as a firm moves through its life cycle, minimal supportis needed. However, financial support is needed across all stages ofdevelopment in order to encourage growth. Mentoring is indicated to be vital atthe early stages of development since a low level of awareness is found amongthe respondents for the services provided by support organizations.(NEE)
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".