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Enforcement Issues Associated with Prospectus Exemptions in Canada

2017· article· en· W6960041411 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsProspectusEnforcementIssuerProduct (mathematics)Investment (military)Commission

Abstract

fetched live from OpenAlex

This paper examines the regime of prospectus exemptions in Canada. The focus is on the enforcement process, and to this end the article includes an empirical examination of enforcement actions by both the securities regulators and the Investment Industry Regulatory Organization of Canada (IIROC). The end in view is two-fold; to provide a description of the exempt market and enforcement efforts, and to comment on whether enforcement resources are properly deployed. The latter necessarily requires a critical examination of the nature of the various exemptions, their purposes, and whether they are appropriately crafted to achieve their desired ends. That in turn requires an analysis of which issuers use the various exemptions, what types of securities are issued, and who the buyers are. Unfortunately, the Canadian data falls far short of painting a comprehensive or reliable picture of the exempt market. This is a product of many factors. Notably, many exempt financings need not be reported to the regulators. Moreover, many small private companies do not report their exempt financings even when required to do so. The extent of the under-reporting problem (from both causes) is illustrated by data from Statistics Canada suggesting that in 2014, there were about 156,000 exempt financings across Canada. However, in that year, only about 7,125 exempt financing reports were filed with the regulators. Understanding the nature of the exempt market is also hindered by the fact that not all of the provincial regulators compile statistics relating to the use of the various exemptions, and even among regulators that provide statistics, these are not published on an annual basis. Added to this, the published statistics that we have fall far short of painting a comprehensive or picture of the exempt market. The regulators do not compile cross-tabulated statistics relating to the nature of the issuers, the types of purchasers, the amount of capital raised, and the types of securities issued. Nor do they keep statistics on redemptions of prospectus-exempt securities, thus potentially yielding a material overstatement of the net amount of prospectus exempt financings each year.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.164
Teacher spread0.153 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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