MétaCan
Menu
Back to cohort
Record W2766594670

Canada Update - Highlights of Major Legal News and Significant Court Cases from February 2009 through April 2009

2009· article· en· W2766594670 on OpenAlexaboutno aff
Andrew C. Brown

Bibliographic record

VenueSMU Scholar (Southern Methodist University) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLegal case studies and regulations
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

HE government of Ontario has recently proposed a tax harmonization plan that would combine the provincial and federal sales taxes on products and services in an effort to make the sales tax system more efficient.'According to the Ontario Finance Ministry, the harmonization would also help make Ontario more competitive in the slumping economy by reducing the cost of goods that the province exports. 2 Under the current system, Canada has a nationwide general sales tax (GST) of five percent with individual provinces imposing their own sales tax rates.3 Ontario's provincial sales tax rate (PST) stood at eight percent at the time of this writing.4 Under harmonization, the dual sales tax will be done away with and Ontarians will pay a single, federally administered sales tax of thirteen percent.5 While the tax harmonization will not change the price of most items, some items that had previously been exempt from the provincial sales tax, such as electricity and professional services, will now be subject to the harmonized tax rate.6 Ontario legal professionals are especially concerned about the new harmonization plan because legal fees, which were already subject to the five percent GST, will be taxed an additional eight percent.7 The Ontario Bar Associ-*J.D.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.056
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.010
Science and technology studies0.0080.001
Scholarly communication0.0070.001
Open science0.0020.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0290.004

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.

Opus teacher head0.018
GPT teacher head0.252
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2009
Admission routes1
Has abstractno

Explore more

Same venueSMU Scholar (Southern Methodist University)Same topicLegal case studies and regulationsFrench-language works237,207