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Record W1608165147 · doi:10.55016/ojs/sppp.v6i1.42418

Fixing Saskatchewan’s Potash Royalty Mess: A New Approach for Economic Efficiency and Simplicity

2013· article· en· W1608165147 on OpenAlexaffabout
Duanjie Chen, Jack Mintz

Bibliographic record

VenueThe School of Public Policy Publications · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPotashSimplicityNatural resource economicsBusinessAgricultural economicsEconomicsPhilosophyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

For a government’s fiscal program to best serve the public interest, while also ensuring sufficient public revenue collection, it has to meet three criteria: efficiency, simplicity and fairness. Unfortunately, the royalty and tax system currently in place for Saskatchewan’s robust potash-mining industry is none of these three things; it has actually reached the point of incoherence and absurdity, or a mess. Under the current royalty and tax regime for potash producers, the tangled thicket of royalties, taxes and credits can differ between commencement dates for production, projects of different sizes, or even projects of similar size but with different profitability; it also has potash producers generally enjoying a much lighter tax burden on marginal investments than that borne by the oil and gas industry and most other non-resource industries. The result is distortions and inefficiencies, resulting in subpar investment activity, which can only stand in the way of Saskatchewan reaching its full economic potential.That needs to change. The Saskatchewan government can implement a simpler and properly structured rent-based tax and a revenue-based royalty as minimum payment (where the royalty can be credited against the rent tax), each with a single rate and an identical tax base for all tax and royalty payers. Such a properly structured royalty and tax system can deliver improved productivity, without reducing investment incentives, resulting in a better partnership for both Saskatchewan’s industry and government in the long run.

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)0.006
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score0.835

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.009
Scholarly communication0.0120.006
Open science0.0030.008
Research integrity0.0030.011
Insufficient payload (model declined to judge)0.0180.003

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 head0.037
GPT teacher head0.307
Teacher spread0.270 · 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
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

Citations2
Published2013
Admission routes2
Has abstractyes

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