MétaCan
Menu
Back to cohort
Record W2898316944 · doi:10.3138/utlj.2018-0036

Group RESPs: The intersection of government support for education savings and securities regulation

2018· article· en· W2898316944 on OpenAlexaffvenueabout
Gail E. Henderson

Bibliographic record

VenueUniversity of Toronto Law Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsIncentiveGovernment (linguistics)BusinessFinancePublic economicsEconomicsMarket economy

Abstract

fetched live from OpenAlex

Tax incentives encourage Canadian families to save for their children’s post-secondary education. In recent years, the federal government has created and enhanced incentives aimed specifically at low- to middle-income families. To access these incentives, families must open a ‘registered education savings plan’ (RESP). Approximately one-quarter of RESPs are invested in group plan RESPs. Group plan providers are regulated by securities laws. Group RESPs have a unique and complicated structure, which generates a high number of consumer complaints, particularly about the high, upfront fees. Group plan providers also have a long history of non-compliance with securities laws, including selling group plans to investors for whom they are not suitable. The combination of high, upfront fees and the lack of suitability is particularly harmful to low-income investors. A bad experience with a group RESP may lead the investor to avoid education savings altogether, thereby undermining the government’s policy goals in establishing incentives for low-income families and, ultimately, affecting the future path that may be taken by the potential beneficiaries of such savings. This article examines government incentives for education savings, the terms of group plans and their history of non-compliance, and puts forward three possible avenues for reform, including decoupling incentives aimed specifically at low- and middle-income families from having to open a RESP.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.993

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 designTheoretical or conceptual
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

Citations3
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueUniversity of Toronto Law JournalSame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207