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Record W6928122331 · doi:10.34874/prsm.jbe.36906

Business Model of a Cannabis Start-up

2023· article· en· W6928122331 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePRSM · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisContext (archaeology)Stock exchangeBusiness modelLiberian dollarVenture capitalStock market

Abstract

fetched live from OpenAlex

This study provides an overview on a major phenomenon in North America: cannabis start-ups raising hundreds of millions of dollars on Canadian Stock Exchanges. The research brings relevant and strategic information on a process where a cannabis start- up, with no solid tangible assets, can have access to venture capital on stock market exchanges. Additionally, our research team explores ten inspiring cases of cannabis Unicorns where multimillion dollar private placements (PP) bring a win/win/win situation for cannabis entrepreneurs, accredited investors and the Canadian economy. Financing of cannabis start-ups through a Reverse-Take-Over (RTO) is briefly explained albeit the enigmatic aspects of the process. The authors’ other challenge is to decipher the complexity of a cannabis business model in an ecosystem where the uncertainty of the legal context is the name of the game. The article also explores the value chain of the cannabis industry. Lastly, the authors identify a game-changer: a new Moroccan law could significantly alter the global cannabis industry and transform the landscape as a whole.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.238
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it