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Record W4378648857 · doi:10.32920/23257610

Improving Groundwater Policy Using an Alternative Economic Policy Instrument in Ontario: Aligning Sustainability Governance with Corporate Groundwater Takings Practice

2023· preprint· en· W4378648857 on OpenAlexaffabout
Taylor Garnet-Abrams

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGroundwaterBottling lineCorporate governanceBusinessSustainabilityResource (disambiguation)Government (linguistics)Water resource managementSurface waterWater qualityEnvironmental scienceNatural resource economicsEnvironmental engineeringEconomicsEngineeringFinance

Abstract

fetched live from OpenAlex

Groundwater is a finite and depletable natural resource. According to the Canadian government, groundwater is the primary source of water for washing and drinking for 28.5% of Ontarians. Over-pumping of groundwater affects its quality and negatively impacts ground and surface water ecosystems. Ontario’s Water Resources Act (1990) stipulates that corporations involved in water bottling apply for permits to pump groundwater if the firm intends to take more than 50,000 litres of water on any given day. The rate that water bottling companies pay for water prevents Ontario from recovering administrative regulatory costs and does not incentivize conservation or signal value for groundwater. This paper examines Ontario’s groundwater policies, agreements and policy instruments relating to water takings and investigates an Inverted Block Rate Structure (IBRS) as an alternative economic policy instrument. The findings suggest an IBRS can efficiently allocate greater controls over Ontario water resources, encourage conservation, thereby improving groundwater governance.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.643

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.155
GPT teacher head0.303
Teacher spread0.149 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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