Creating the Entrepreneurship & Libraries Conference 2020: A Collaboration of Public, Special, and Academic Librarians, Vendors, and Economic Development Stakeholders
Bibliographic record
Abstract
Despite the increasing importance to libraries of supporting entrepreneurship and economic development, professional development opportunities on those topics have been rare. Also rare are opportunities for public, special, and academic librarians plus other types of professionals to collaborate on major professional development events like a multi-day conference. The authors and a diverse planning group worked to challenge that status quo by creating the Entrepreneurship & Libraries Conference (ELC) 2020. After making a COVID-19-mandated pivot to an online format, this conference featured speakers, networking hours, a discussion room hour, and a pitch competition with cash prizes for libraries proposing economic development projects. This article describes how a diverse group of librarians and economic development stakeholders from across the United States and Canada worked together to define, develop, and lead the ELC 2020. The article concludes with assessment and recommendations.
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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 | 0.033 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.005 |
| Scholarly communication | 0.030 | 0.009 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".