The Invisible Industry: Resources for Supporting Cannabis Entrepreneurs (Entrepreneurship & Libraries Conference 2021)
Bibliographic record
Abstract
Column introductionBusiness librarians need to be nimble to discover and document new industries and services that our students are seeking.While we have focused historically on new innovations (location-based services, fintech, etc.), an equally important element is learning about recently legalized industries.This latter group would definitely include the quickly growing (no pun intended) cannabis industry.Steven Cramer, Morgan Ritchie-Baum and Andrea Levandowski share this report from a great workshop hosted as part of the Entrepreneurship & Libraries Conference, which brings together business libraries who support entrepreneurship in their schools and communities.-Corey Seeman, Column Editor About the WorkshopIn the final networking happy hour of the Entrepreneurship & Libraries Conference (ELC) 2020, attendees expressed interest in a workshop focusing on the "entrepreneurship of sin," shorthand for the growth industries of microbreweries, distilleries, and recreational marijuana.Later, the planning group of the ELC 2020 proposed the theme of "entrepreneurship, libraries, and cannabis" as a possible ELCsponsored workshop in Spring 2021.As discussed in Ritchie-Baum, Thynne, and Cramer (2021), the ELC is an official service of BLINC: Business Librarianship in North Carolina (https://nclaonline.wildapricot.org/BLINC),although the ELC planning group includes public, academic, and special librarians from across the United States and Canada. Nature of the Workshop"The Invisible Industry: Resources for Supporting Cannabis Entrepreneurs" (https://entrelib.org/schedule-at-a-glance-3/) was an online workshop hosted via Zoom on May 27, 2021.Attendance was free thanks to sponsorship by Mintel, PrivCo and EveryLibrary.A total of 259 people registered from a variety of organizations, including academic, public, and special libraries; the cannabis industry; and economic development offices.The workshop's three-and-a-half hours of programming
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.029 |
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".