Bibliographic record
Abstract
The internet has significantly changed the way work, education, and funding operate. Examples include opportunities for online distance work, crowdsourcing/crowdfunding, e-commerce, peer-to-peer networks, and online learning opportunities. To succeed in these new ventures, individuals will benefit from building skills in business, research and entrepreneurship, updated to focus on new trends in the workforce. Skills such as identifying a market, budgeting, promotion, or writing a business plan support workers, whether their business is online or in their communities. Online learning opportunities can especially benefit rural communities, which may not be able to support traditional in-person delivery of training, such as diploma or certificate programs, apprenticeships, or mentor networks. This session will focus primarily on the Small Business Accelerator as a channel for the information and training needed to build capacity and adapt to a changing economy. The SBA is an open resource, delivered online and aimed at small & medium enterprises (SMEs), entrepreneurs, and business owners in British Columbia. It provides instruction on business research skills, step by step guides for various industries, as well as a directory of small business support. It offers free online access to reliable business information and tools for secondary market research for BC businesses and entrepreneurs. Our goal is to promote BC business and communities as well as facilitate partnerships and collaborations. Come to this session and learn more!
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.030 |
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".